Nirmukta » Vinod Kumar Wadhawan

COMPLEXITY EXPLAINED: 15. Evolution of Cultural Complexity

Vinod K. Wadhawan — Fri, 26 Feb 2010 09:34:52 +0000

(Note: All previous parts in the Complexity Explained series by Dr. Vinod Wadhawan can be accessed through the ‘Related Posts’ listed below the article.)

Man invented language to satisfy his deep need to complain, opined Lily Tomlin. On a more serious note, the evolution of language, speech, and culture are believed to be some of the causative factors for the rapid evolution of the size and capacity of the human brain. The emergence of human language has been a major milestone in the relentless evolution of complexity on our planet, and has also played a role in the evolution of human consciousness. Apart from the emergence and evolution of language, I also discuss memetics and econophysics in this article.

15.1 Introduction

A mostly Lamarckian process whereby evolution of a transformational nature proceeds via the passage of acquired characters, cultural evolution, like the stellar evolution before it, involves no DNA chemistry and perhaps less selectivity than biological evolution. Culture enables animals to transmit survival kits to their offspring by nongenetic routes; the information gets passed on behaviourally, from brain to brain, from generation to generation, the upshot being that cultural evolution acts much faster than biological evolution.

Eric Chaisson, Cosmic Evolution

According to Richard Dawkins (1989), ‘most of what is unusual about man can be summed up in one word: “culture”.’ Of course, one must make a distinction between ‘culture’ and ’society.’ ‘A society refers to an actual group of people and how they order their social relations. A culture . . . refers to a body of socially transmitted information’ (Barkhow 1989). The term ‘culture’ encompasses ‘all ideas, concepts and skills that are available to us in society. It includes science and mathematics, carpentry and engineering designs, literature and viticulture, systems of musical notation, advertisements and philosophical theories - in short, the collective product of human activities and thought’ (Distin 2005).

15. 2 Evolution of Language

If there had been no speech, then right and wrong, truth and falsehood, good and bad, attractive and unattractive would not have been made known. Speech makes known all this. Worship speech.

Chandogya Upanishad VII-2-1

As far as humans are concerned, language has got to be the ultimate evolutionary innovation. It is central to most of what makes us special, from consciousness, empathy and mental time travel to symbolism, spirituality to morality.

Kate Douglas

Somewhere in the last 100,000 years or so, human beings hit upon language. Human language must have seemed an odd-sounding innovation to the other animals around. But by allowing the expression of arbitrarily complicated concepts, human language allowed people to process information in a highly distributed fashion. The distributed nature of human information processing in turn allowed people to cooperate in new ways, forming groups, associations, societies, companies, and so on. Some of these new forms of cooperation proved strikingly effective, as various forms of distributed information processing, such as democracy, communism, capitalism, religion, and science, took on a life of their own, propagating themselves and evolving over time. It is the richness and complexity of our shared information processing that has brought us this far.

Seth Lloyd, Programming the Universe

It is notable that, on an evolutionary time scale, there has been an exceptionally rapid expansion of brain capacity in the course of evolution of one of the ape forms (chimpanzees?) to Homo sapiens, i.e. ourselves. This has happened in spite of the fact that the genome of humans is incredibly close to that of chimpanzees. The evolution of language, speech, and culture are believed to be some of the causative factors for this rapid evolution of the human brain. Let us see how.

Homo sapiens was preceded by Homo heidelbergensis, which also had a fairly large brain, but was not very effective as a hunter. He was not able to establish ecological dominance over other animals, even after two million years of evolution. The human advantage is believed to have arisen from the emergence of language. ‘No topic is more intriguing and more difficult to address concretely than the evolution of language, but … [it] is almost a kind of sixth sense, since it allows people to supplement their five primary senses with information drawn from the primary senses of others. Seen in this light, language becomes a kind of “knowledge sense” that promotes the construction of extraordinarily complex mental models, and language alone may have provided sufficient benefit to override the cost of brain expansion’ (Klein and Edgar 2002).

The reference to ‘the cost of brain expansion’ here is to the fact that in humans the brain takes up ~20% of the metabolic resources of the body, and the brain tissue requires 22 times more energy than a comparable piece of muscle at rest.

Deacon (1997) emphasizes the big difference between human language (talking) on one hand and the various modes of communication among other live entities: ‘Although other animals communicate with one another, at least within the same species, this communication resembles language only in a very superficial way - for example, using sounds - but none that I know of has the equivalents of such things as words, much less nouns, verbs, and sentences. Not even simple ones.’

Deacon (1997) continues: ‘Though we share the same earth with millions of living creatures, we also live in a world that no other species has access to. We inhabit a world full of abstractions , impossibilities, and paradoxes … We tell stories about our real experiences and invent stories about imagined ones, and we even make use of these stories to organize our lives. In a real sense, we live our lives in this shared virtual world. … The doorway into this virtual world was opened to us alone by the evolution of language, because language is not merely a mode of communication, it is also the outward expression of an unusual mode of thought - symbolic representation. Without symbolization the entire virtual world is … out of reach: inconceivable … symbolic thought does not come innately built in, but develops by internalising the symbolic process that underlies language.’

Homo heidelbergensis had a big brain. But was he also a great symbolic thinker? Probably not. Deacon argues that probably a single symbolic innovation triggered a coevolution of language and brain-size. Greater brain power resulted in a greater capacity to symbolise, speak, think. The cascading effect led to more complex languages and more complex brains. But all this required social interaction and support: ‘Language is a social phenomenon. … [and] … The relationship between language and people is symbiotic.’

Deacon traces the evolution of social complexity by assuming that the early humans were dual-parenting. Since their sense of smell was not very acute (thus ruling out a role for chemical signalling through pheromones), some other type of sexual signalling evolved between the male and the female. This is how social communication originated and evolved as a kind of social hormone.

Other than sex, availability of food is the major factor determining the survival of a species. Males had to cooperate with one another for hunting. Deacon again: ‘Males must hunt cooperatively; females cannot hunt because of their ongoing reproductive burdens; and yet hunted meat must get to those females least able to gain access to it directly (those with young), if it is to be a critical subsistence food. It must come from males … [who] … must maintain constant pair-bonding relationships.’

This need for hunting in groups resulted in the evolution of a social structure implying a symbolisational solution to the problem of survival. This is because symbolic reference, as also speaking and thinking, are basically of a social nature. There was naturally a concomitant evolution of the speech organ (voice box).

Grooming

According to Robin Dunbar, ‘One of the most important ways that primate allies show their affection to each other is by grooming. Grooming not only gets rid of lice and other skin parasites, but it also is soothing. Primates turn grooming into a social currency that they can use to buy the favour of other primates. But grooming takes a lot of time, and the larger the group size, the more time primates spend grooming one another. Gelada baboons, for example, live on the savannas of Ethiopia in groups that average 110, and they have to spend twenty percent of their day grooming one another. … If we had to bond our groups of 150 the way primates do, by grooming alone, we would have to spend about 40 to 45 percent of our total daytime in grooming.’

The primates in the savannas also had to find food, and therefore such a large investment of time in grooming would have caused a non-sustainable work vs. life balance. Language emerged as a better way of bonding.

Evolution of word-speaking species

Humans began with sound language, gradually increasing the vocabulary. But there is a severe limit to how many sound calls you can have which still sound distinct. The next step in the evolution of language was a stringing together of sounds into specific sequences, namely words. Word-speaking species naturally had an evolutionary advantage.

Sentences syntaxing words were the next level of evolving complexity. Brain size increased concomitantly to understand and remember words, syntax, grammar, and sentences (Zimmer 2001): ‘A syntax-free language beats out syntax when there are only a few events that have to be described. But above a certain threshold of complexity, syntax became more successful. When a lot of things are happening, and a lot of people or animals are involved, speaking in sentences wins … Something about the life of our ancestors became complex and created a demand for a complex way in which they could express themselves … A strong candidate for that complexity, as Dunbar and others have shown, was the evolving social life of hominids.’

This social evolution of complexity is the advantage humans have over other animals. They have the capacity to introduce and expand complexity in social life, and development of language is both a cause and an effect of this capacity. As Kate Douglas (2005) said, ‘In a sense, language is the last word in biological evolution. That’s because this particular evolutionary innovation allows those who possess it to move beyond the realms of the purely biological. With language, our ancestors were able to create their own environment - we now call it culture - and adapt to it without the need for genetic changes.’

Whereas humans and chimpanzees have many genes in common, the expression of certain genes is more common in the human brain. Moreover, the brains of newborn humans are far less developed than those of newborn chimpanzees, and the neural networks of human babies are developed over many years of exposure to a linguistic environment. Through a continuous process of unsupervised learning (experimentation), supervised learning (from parents, teachers, etc.), and reinforced learning (the hard-knocks of life, and rewards for certain kinds of action), the child’s brain performs evolutionary computation.

With language came the possibility of emergence of ‘memes.’ Language coevolved with memes.

15.3 Memes and Their Evolution

We are different from all other animals because we alone, at some time in our far past, became capable of widespread generalized imitation. This let loose new replicators - memes - which then began to propagate, using us as their copying machinery much as genes use the copying machinery inside cells. From then on, this one species has been designed by two replicators, not one. This is why we are different from the millions of other species on the planet. This is how we got our big brains, our language and all our other peculiar ’surplus’ abilities.

Susan Blackmore (2000)

It is information that evolves in any type of evolution. The most basic aspects of evolution are replication of information (which involves preservation of the replicated information), and the mode of transmittal of information. Genes preserve biological information, and they use DNA for this. What about culture?

Similar to the gene, which is the unit of biological inheritance, Richard Dawkins (1989, 1998) introduced the notion of the meme, which is the unit of cultural inheritance. A meme may be some good idea, a soul-stirring tune, a logical piece of reasoning, or a great philosophical concept. Dawkins visualized that two different evolutionary processes must have operated in tandem: the classical Darwinian evolution, and another one centred round intelligence, language, and culture. Memes are, roughly speaking, the cultural analogues of genes.

The genes that exist in many copies in a population are those that are good at surviving and replicating. Through a reinforcement effect, genes in the population that are good at cooperating with one another stand a better chance of surviving. Similarly, the fittest set of cooperating memes has a better chance of surviving to form the meme pool of the population. They replicate themselves by imitation or copying (Blackmore 1999, 2000), and also by a variety of other mechanisms (Distin 2005). Cultural evolution and progress occurs through a selective propagation of the fittest set of cooperating memes.

Meme-gene coevolution

Memes evolve, just as genes evolve. In fact, any entities that can replicate, and that have a variation both in their specific features and in their reproductive success, are candidates for Darwinian selection. The coevolution of gene pools and meme pools (through language etc.) resulted in a rapid enlargement of the brain size of Homo sapiens. A large brain size, once attained, resulted in several other capabilities as well.

An important difference between memes and genes is that the speed of cultural evolution (development of ideas, customs, etc.) is far higher than the speed of genetic evolution. Nevertheless, there are several proposed analogies between the two. How far can we carry the gene analogy for understanding the nature of memes? This continues to be a subject of debate. Following Distin (2005), I list here some characteristics of memes.

The essential particulate nature of memes

The most efficient methods of replicating complexity are hierarchical (or modular or particulate). If variation were permitted in every element of a complex structure, then copying processes would lose much of their stability. In genetics, Mendel’s work established the particulateness of genes, namely the clear presence or absence of the effects of these replicators on the world. Something similar is necessary for memes in their role in cultural evolution of complexity. This means that memes must be able to fit into established cultural assemblies without their own informational content being lost or blended in the process. That is, memes must have a certain degree of particulateness, so that the results that they produce are generally of a fixed nature. Their identity should be such that they are discernible packets of information (like the genotype). But, whereas the genotype is distinct and clearly definable, the phenotype (which is a manifestation of the genotype) in biological systems possesses a certain degree of flexibility and variability. Likewise, the manifestations of memes have a certain degree of flexibility that enables their effects to be produced in a variety of cultural contexts. Copied in these ways, information is given the stability to grow and develop in complexity. The breadth and depth of human culture is thus explained by the cumulative replication of particulate information.

In both genetics and memetics, the replicators carry information about the effects that they control. In the case of genes, their independence is maintained via the medium of DNA, which preserves biological information in a form that is replicable and can produce its effects in a variety of contexts. In the case of memes, this role is performed by ‘representational content,’ which is thus the memetic or cultural equivalent of DNA.

Representational content of memes

Memes are specified by their representational content. As representations of a portion of information, memes can be regarded as having a certain content. A representation in the human mind is some piece of our ‘mental furniture’ that carries information about the world. For example, a thought that ‘the object on my desk is a book’ is a mental representation of a bit of the world (i.e. that book). Therefore ‘representational content’ refers to the information that is included in the content of our representations.

It is representational content which accounts for the mechanisms of memetic heredity and for the influence of the memes over their phenotypic effects. Distin (2005) uses the term memetic DNA for the representational content. It provides the mechanism for memetic evolution, just as DNA provides the mechanism for genetic evolution.

How is the representational content fixed in our brains?

Replicators preserve and copy specific portions of information. For memes, we should be able to identify precisely which bits are carried in each replicator. This means pinpointing the exact content of any representation, and this is something determined partly by the various properties of the object or situation being represented. Yet representational content is determined by other factors as well, e.g. by the capabilities and history of the organism doing the representing.

Some organisms are capable of forming representations the content of which is determined by a combination of the relevant properties of that which is represented, and the organism’s own individual and social learning capacities. Such organisms are able, in other words, both to preserve information and to transmit it among themselves.

In the case of complex representations, which have links not only externally to perceptions and behaviour but also internally to other representations, the resultant behavioural flexibility can enable us to track down their content more completely. It should be possible to test all of the links, by altering the associations that the organism encounters, and observing the effects on its behaviour. Only representations with this determinacy of content can count as memes, since a crucial aspect of any replicator is the preservation of given information.

Thus memes are representations which preserve their content in a way that can be copied between generations. As representations, they are specifically those bits of our mental furniture which control our behaviour in response to the information that they carry. In other words, the basis of memes in representational content is precisely what accounts for their ability to exert executive effects on the world.

Multiple representational systems

Representations gain meaning from their context within a representational system (RS), and the uniquely human capacity that lies at the heart of culture is our ability to copy and develop RSs, as well as adding individual representations to our repertoire: the ability, in other words, to meta-represent. Natural languages, as also systems of mathematical and musical notation, are some examples of cultural RSs, and each is peculiarly appropriate to its particular cultural area. Human minds acquire replicators on an ongoing basis throughout their lives, and this means that they can acquire novel RSs as well as novel representations. Amongst these various RSs, the natural languages have primacy: they alone benefit from an innate device for their acquisition. Yet they benefit, too, from the innate ability to meta-represent - and it is this which allows us also to develop nonlinguistic RSs, whose diverse rules and structures are realized in media other than speech. Once these sorts of RS have been taken into account, it becomes clear that there are many concepts that are not available to us until the RS that supports them has been developed.

Human minds and culture

According to Distin (2005), humans are born with a degree of mindedness that includes, for example, the ‘representation instinct’: an ability and tendency to learn and manipulate vast numbers of representations, as well as the various systems in which they are embedded. And this innate mental potential of an infant is realized as a result of exposure to the cultural environment.

Genes preserve and replicate biological information by building vehicles for their own propagation and protection. The effects of the genes are found in the machines that they build for their survival, and their replication also depends ultimately on this same machinery. Memes depend for their replication on a faculty of the human mind that is ultimately of a genetic nature, namely the representation instinct. Organisms, as well as minds, develop via interaction between the innate potential and the environment, and in the case of the mind a crucial part of that environment comprises of the memes. A human mind is thus partly a product of the memes, but only because it has the innate potential to interact with and develop in response to these memes. And culture is the product of human minds, although the preservation of information in representational content ensures that the culture we see today is mostly the result of memes produced by human minds of long ago. The development of human minds depends on a combination of two types of processes: their innate potential is the result of an interaction between genes and the physical environment, and that potential is fulfilled as a result of interaction with memes.

The selfish meme?

Memes are best thought about not by analogy with genes but as new replicators, with their own ways of surviving and getting copied.

Susan Blackmore

Dawkins (1989) described the essence of his ’selfish gene’ theory as the insight ‘that there are two ways of looking at natural selection, the gene’s angle and that of the individual.’ The essence of his selfish meme hypothesis is the insight that there are two ways of looking at cultural change, the meme’s angle and that of the human individual.

One of the most significant implications of the theory of Dawkins’ selfish gene is that the individual organism was not an inevitable outcome of genetic evolution: it so happens that genes have banded together to build survival machines, but the only crucial feature of any form of evolution is the replicator - the unit of selection. Although organisms clearly exist, and have a perspective from which the world of genes is irrelevant to their everyday lives, fundamentally their lives and evolution are determined by that world. According to Distin (2005), no analogous insight arises from the theory of the selfish meme, because memes do not build survival machines. Their replicative mechanisms, and the means of their variation and selection, lie in genetically determined human faculties, and not in vehicles that they themselves build.

Dennett (1991) and Blackmore (1999), however, take the view that we are meme machines, just as we are gene machines. Consequently, they argue that ‘there is no conscious self inside’ those machines; and that ‘a complex interplay of replicators and environment’ is all there is to life. Our sense of self may not be illusory, but our sense of control over the collective products of our minds may well be. Although our minds provide the mechanisms of memetic evolution, there is a very real sense in which the directions of that evolution are independent of us.

15.4 Econophysics

A financial market is a complex system in which a large number of traders interact with one another, and also react to external information, and determine the ‘best’ price for a given item. The time evolution of the price and the number of transactions of a traded item is generally unpredictable. The time series indicating the price variation of an item is found to be essentially indistinguishable from a stochastic or random process. Like other complex systems, financial markets are open systems with many interacting subunits, and the subunits interact nonlinearly.

The efficient-market hypothesis

An efficient market is defined as one in which all the available information is processed instantly when it reaches the market, and in which this fact is immediately reflected in new values of prices of the traded assets. The efficient-market hypothesis (EMH) says that any market is highly efficient in determining the most rational price of a traded item or asset. It was originally formulated in the 1960s. There are two assumptions involved here: (i) the market is efficient; and (ii) the behaviour of traders is strictly rational.

Why does the time series of returns appear to be random? This is because it carries so much information that there are no readily discernible regularities in it. It is, by and large, a nonredundant time series. The information it carries is almost irreducible, or algorithmically incompressible for most practical purposes (cf. Part 4). The EMH requires that the concerned time series for market prices has a dense amount of nonredundant information. Since there are limits on the speed and capacity of our computers, a time series carrying this information is almost indistinguishable from a totally random time series. Of course, analysis of the deviation from a totally random time series is a good way of testing the degree of validity of the EMH in a given situation.

The law of diminishing returns

Suppose there is good demand for a commodity because of its attractive existing price. Naturally, the price will increase. This will then reduce the demand. And a reduced demand will entail a lowering of the price, and so on, till the demand and the price have reached a state of equilibrium. Thus negative feedbacks tend to stabilize an economy, as per conventional economic theory. This law of diminishing returns implies a single equilibrium point for an economy, and such situations are amenable to analytical control.

By and large, resource-based economic activities (e.g. agriculture and mining) tend to follow the law of diminishing returns. By contrast, knowledge-based parts of an economy are generally governed by the law of increasing returns or positive feedback.

The law of increasing returns

As demonstrated by the pioneering studies of Brian Arthur during the 1990s, positive feedbacks often occur in an economy, with the resultant multiple equilibrium points. Small shifts in the economy can get amplified, rather than smothered out. The economy evolves like any open, nonlinear complex system. There can be multiple bifurcations in phase space, and it is difficult to predict which bifurcation branch will be chosen by the market forces. What is more, once the random events select a particular branch or path in phase space, the choice tends to get locked-in, regardless of the advantages of the alternatives. An example is the history of the VCR industry. The market started out with two competing formats, VHS and Beta, selling at about the same price. It appears, in hindsight, that Beta was technically superior. In the beginning there were increasing returns for each format, as their market shares increased. For example, a large number of VHS recorders in the hands of consumers motivated the vendors to stock more prerecorded tapes in the VHS format. This encouraged more people to buy VHS recorders. The same law of increasing returns operated for the Beta format also. In the beginning there were fluctuations in the fortunes of the two competing brands, attributable to factors such as external circumstances, ‘luck’, and corporate manoeuvring. Then, perhaps by chance, increasing returns on early gains by VHS (reduced production costs per unit on increased volumes of production) tilted the game in favour of VHS, driving the other technology out of the market. This is something which could not have been predicted in the beginning.

The law of increasing returns can go beyond the product with which a company started (Arthur 1990): ‘Not only do the costs of producing high-technology products fall as a company makes more of them, but the benefits of using them increase. Many items such as computers or telecommunications equipment work in networks that require compatibility. When one brand gains a significant market share, people have a strong incentive to buy more of the same product so as to be able to exchange information with those using it already.’

Path dependence

In a positive-feedback economy, although the individual transactions are small and essentially random events, they can accumulate by the positive (nonlinear) feedbacks. A number of characteristics or historical antecedents of positive-feedback economies can be listed:

1. In a particular industry, there is often a clustering of firms in a specific geographical location. A different location would have been better, but there is a kind of freezing of historical accidents in what has actually happened. Why? The first firm chooses a location for some logical (or even illogical) reason. The choice of the second firm depends not only on the (real or perceived) merits of that region, but also on the fact that it is profitable to be near the first firm. There is cascading effect because the third firm may be influenced more by the presence of the first two firms in that region, than by the absolute merit of that region; and so on.

2. Railroad gauges are what they are at present because, once a particular choice was made (even arbitrarily), it was economical to stick to that choice everywhere in that region. There is a self-enforcement effect operating here.

3. The initial advantage possessed by a country or a multinational corporation can snowball into total dominance at the global level, until a better or cheaper product overcomes the monopoly. This highlights the importance of industrial research in any knowledge-based economy. Anther important factor is the timing of release of a product.

In the language of evolution of the phase-space trajectory, what we are seeing here are random bifurcations in phase space. Once a branch of a bifurcation gets selected for further time-evolution, there is no going back; there is only a locked-in trajectory along a particular path in phase space. Thus the evolution of a positive-feedback economy has a strong path dependence. This path dependence can cause even hitherto successful economies to become locked into inferior paths of development. There is always a danger that a sound technology, with good long-term potential, may get rejected just because it has a long gestation period and slow initial growth. Similarly, when two new technologies compete, the one with a better initial acceptance by people may oust the other from the market, even when the other technology is inherently better (as shown by later events). Early superiority or ’selectional advantage’ is no guarantee of long-term fitness. Arthur (1990) cites the example of how the U.S. nuclear-power programme got ‘phase-locked’ into the light-water-cooled reactors option, even though the high-temperature, gas-cooled, reactor designs may be inherently superior.

The bottom line is that, unlike negative-feedback economies, positive feedback economies do not head for a unique equilibrium; their phase-space trajectory is not path-independent. Like in a chaotic system, even identical-looking initial conditions can lead to divergence in trajectories, simply because even small events or errors may get hugely amplified as time passes. Long-term accurate forecasting then becomes difficult, if not impossible.

15.5 Concluding Remarks

The emergence of humans has sharply accelerated the rise of overall complexity of our Earth. This has happened, and is still happening at an ever-increasing pace, because of the evolution of cultural complexity. A major reason for this is the very high level of intelligence possessed by humans. I shall discuss intelligence and consciousness in the next article in this series.

Human beings and their institutions process more energy per unit mass than do stars or galaxies.

Eric Chaisson, Cosmic Evolution

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

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COMPLEXITY EXPLAINED: 14. Biological Complexity at the Edge of Chaos

Vinod K. Wadhawan — Tue, 02 Feb 2010 05:39:58 +0000

(Note: All previous parts in the Complexity Explained series by Dr. Vinod Wadhawan can be accessed through the ‘Related Posts’ listed below the article.)

Living entities have evolved to possess enormous amounts of order and complexity. Can Darwinian natural selection alone explain this order? Probably not. We must also take note of the inherent tendency of all complex adaptive systems to move towards self-organized states of optimum order. Biological and other kinds of complexity thrive best at the ‘edge of chaos,’ and this is where evolutionary forces usually operate. The dynamics of complexity around the edge of chaos is ideally suited for evolution that does not destroy self-organization.

14.1 Introduction

Self-organization is a characteristic feature of any open, far-from-equilibrium complex system. As emphasized by Stuart Kauffman, it is on this existing order that Darwinian natural selection operates and further adapts it to the environment. In other words, natural selection is not the sole source of order in biology. Being complex systems, biological entities tend to self-organize anyway. As Kauffman said in At Home in the Universe (1995):

I suspect that the fate of all complex adaptive systems in the biosphere — from single cells to economies — is to evolve to a natural state between order and chaos, a grand compromise between structure and surprise. Here, at this poised state, small and large avalanches of coevolutionary change propagate through the system as a consequence of the small, best choices of the actors themselves, competing and cooperating to survive.

He mentions ‘chaos.’ Let us begin by getting familiar with some elementary concepts in chaos theory.

14.2 Elements of Chaos Theory

A chaotic system is characterized by unpredictable evolution in space or time, even though the differential equations or difference equations describing it are deterministic (if we can neglect noise). The motions in a chaotic system are unstable, and this instability leads to a sensitive dependence on initial conditions. In the language of algorithmic information theory (cf. Part 4), chaos has the largest (but finite) degree of complexity.

Several basic features of chaos can be illustrated by recourse to the so-called logistic equation. It embodies a 1-dimensional feedback system, and was formulated as early as in 1845 to model the population dynamics of a species. The question posed was: How will the population of a species, confined to a certain geographic area, vary from year to year? Obviously, the population in a year t+1 will depend on the population in the previous year t: x_t₊₁ = kx_t; here k is some suitable constant of proportionality (or a control parameter). This equation simply models the fact that the larger the population is, the more is the number of offspring it would produce. But this cannot be the full story. There are other factors to consider. For example, if the population in the year t becomes too large, there can be an extra decimation of the numbers, either by predators, or due to shortage of food, or due to the altered competition in the reproduction dynamics. Therefore a more realistic logistic equation is as follows:

_x_t₊₁₌_{k x}_t_(1-_x_t₎

Even this is only a simplistic model of population dynamics, and more sophisticated models have been proposed and investigated. But it is sufficient for providing a good insight into how chaos sets in under certain conditions.

It is convenient to describe the population x as a fraction of the largest value, N, that it can attain. Then x varies between 0 and 1. For any fixed value of k, a plot of x_t₊₁ against x_t gives an inverted parabola, and x_t = 0.5corresponds to the top point on the parabola. Putting x_t = 0.5 in the above equation gives x_t₊₁ = k/4. Since x cannot be larger than 1, the largest value that the control parameter k can have is 4.

A fascinating variety of dynamics, including chaos, is observed as k takes various values in the range 0 to 4. Suppose we imagine a situation for the population in which k is less than 1. Suppose x₀ is the value of the population in a particular year t = 0. Then we can use the logistic equation to calculate the expected population x₁ in the next year. Then x₁can be put back into the logistic equation to obtain the population value x₂ for the following year. We can carry out such iteration repeatedly.

Figure (a) shows how the population will change in successive years if we take k = 0.95. We find that the population eventually becomes zero. This eventual or final value of x, denoted by x*, is an attractor; attractors were introduced in Section 12.4 (Part 12). There is a basin of attraction such that every starting value x₀ is eventually drawn towards the attractor x* = 0. Thus if k < 1, we have a fixed-point attractor at the zero value of the population; the conditions are too inimical for the population to survive.

A different population dynamics is predicted by the logistic equation for 1 < k < 3. Now x* is not zero; rather it increases from a near-zero value to ~0.667 as the control parameter k is increased from 1 to 3. Figure (b) shows the results for k = 2.8.

A fundamentally different kind of dynamics emerges for 3 < k < 4: The population trajectory no longer converges to a single fixed point or attractor in phase space. Further, the trajectory becomes increasingly sensitive to the value of k. For k = 3.4 (results shown in Figure (c)), the trajectory has not one but two fixed points: one at x₁* ≈ 0.452 and the other at x₂* ≈ 0.842. This means that, from year to year, the population oscillates between ~45% and ~84% of the maximum possible value. We now have a two-point attractor (Figure (c)). We describe such an oscillating system as having a period 2.

This periodic attractor is actually just the beginning of still more complex dynamics as the value of k is increased. There is a critical value k ≈ 3.4495 beyond which we get a four-point attractor. For example, for k = 3.5 we get x₁* ≈ 0.875, x₂* ≈ 0.383, x₃* ≈ 0.827, x₄* ≈ 0.501. Such successive bifurcations of each attractor into two, such that there are 4, 8, 16, 32, .. etc. fixed points, occur with smaller and smaller increases in k.

We move into the chaotic regime of complexity for values of k above ~3.57. The periods now double every time k is increased by even an infinitesimally small amount. The number of points that comprise the attractor is now extremely large, and the trajectory looks quite erratic (Figure (d)), although there are some ranges of k values for which there is apparent stability. I have taken these numbers and the figures from the book Chaos Theory Tamed by G. P. Williams (1997), which should be consulted for many other fascinating details.

If the periods are going to double even for infinitesimally small increases in the value of k, there are two situations to face, one practical and the other computational. The practical aspect is that the logistic equation, or any mathematical model for explaining reality, has to finally interface with (or explain) experimental data, and such data can never be obtained with infinite accuracy. The computational aspect is that, even if we have access to infinitely accurate data, no computer can perform calculations based on the modelling equation(s) with infinite precision. It is irrelevant whether or not the computer program written for modelling a complex system is a simple one. This is why one makes the statement that a chaotic system, or rather a system in a chaotic regime, has the largest (though finite) degree of complexity.

Having achieved a nodding acquaintance with chaos theory, let us now hark back to the work of a stalwart in the field of complexity, namely Stephen Wolfram.

14.3 Wolfram’s Four Universal Classes of Cellular Automata

I introduced Wolfram’s work on cellular automata (CA) in Section 11.4 (Part 11). An extensive empirical analysis by him of all 1-dimensional CA showed that the patterns generated by them (even when we start from random or disordered initial conditions) can be generally divided into four distinct classes:

In Class 1, evolution from almost any initial state leads finally to a unique homogeneous state. This is like the occurrence of a ‘limit point’ or attractor in the phase space of a nonlinear dynamical system.

In Class 2, there is ultimately a sequence of simple stable or periodic structures. This corresponds to the occurrence of ‘limit cycles’ in phase space.

Class 1 patterns are repetitive, and Class 2 patterns are nested. Both are predictable after their repetitive or nested nature has been discerned, and are therefore computationally reducible. The black and white figure I showed in Section 11.4 is an example of a class 2 CA, and is reproduced here again.

Image Source : wolframscience.com

Class 3 CA exhibit chaotic or aperiodic long-time behaviour. Such CA grow indefinitely at a fixed speed. Their patterns are often self-similar or scale-invariant. They are characterized by a fractal dimension, with log₂3 or ~1.59 as the most common value for the dimension.

Class 4 CA are the most interesting from the point of view of complex behaviour. For them the pattern grows and contracts irregularly. There are complicated localized structures, some of which propagate with time. Therefore their long-time behaviour is undecidable.

14.4 Langton’s ‘Edge of Chaos’ Idea

Evolution thrives in systems with a bottom-up organization, which gives rise to flexibility. But at the same time, evolution has to channel the bottom-up approach in a way that doesn’t destroy the organization. There has to be a hierarchy of control - with information flowing from the bottom up as well as from the top down. The dynamics of complexity at the edge of chaos seems to be ideal for this kind of behaviour.

Doyne Farmer

I introduced Neumann’s self-reproducing CA in Section 11.6. Christopher Langton (1989) (and also Norman Packard) extended the CA approach to the field of artificial life (AL) (cf. Section 10.4 in Part 10) by introducing evolution into the Neumann universe. In the self-reproducing CA created by Langton, a set of rules (the GTYPE) specified how each cell interacted with its neighbours, and the overall pattern that resulted was the PTYPE. The local rules could evolve with time, rather than remaining fixed. This pioneering work was a fine example of adaptive computation.

Langton also correlated his work on AL with Wolfram’s four universal classes of CA. We have seen above that small values of the control parameter k in the logistic equation give rise to nonchaotic behaviour. This is similar to the dynamics described by Wolfram’s Class 1 and Class 2 CA. And sufficiently large values of k result in totally chaotic dynamics, which corresponds to Class 3 CA. Langton investigated the introduction of a parameter similar to k into the rules controlling CA behaviour to check this analogy more clearly, and particularly to investigate the connection between Class 4 CA on one hand, and partially chaotic systems on the other.

After a number of trials, he came upon a parameter λ for the CA rules which corresponded to the control parameter k of the logistic equation. This λ was defined as the probability that any cell in the CA will be ‘alive’ after the next time step. In the nested CA figure above, we have chosen the colours black and white, which we can now relate to ‘alive’ and ‘dead.’ For example, if λ = 0in the rule governing the evolution of a particular set of CA, all cells would be white or dead after one time step. The same would be true if λ = 1.

In his computer experiments, Langton found that, as expected, λ = 0 corresponds to Class 1 rules. The same was true for very small nonzero vales of λ.

As this control parameter was increased gradually, Class 2 features started appearing at some stage, with characteristic oscillating behaviour. With increasing values of the control parameter, the oscillating pattern took longer and longer to settle down.

Taking λ = 0.5resulted in totally chaotic behaviour, typical of the Wolfram Class 3.

Langton found that clustered around the critical value λ ≈ 0.273were Class 4 CA.

Thus, as the control parameter increased from zero onwards, he saw a transition from ‘order’ to ‘complexity’ to ‘chaos.’

The next conceptual jump Langton made was to equate this qualitative change of behaviour of CA with a phase transition. Recall that a phase transition can occur in a material when some control parameter like temperature is varied (e.g. water changes to ice as it is cooled through its freezing point). Langton realized that his control parameter λ plays a role in determining the dynamics of CA that is similar to the role played by temperature in a phase transition. At low temperatures a material is solid, say in a crystalline state, which is an ordered state. At high temperatures we have a fluid state (liquid or vapour), which signifies chaos or disorder. Langton drew the analogy with such phase transitions for describing the Class 4 behaviour in CA which sets in for values of λ around 0.273.

Phase transitions in a material are represented in phase diagrams, in which there are lines or boundaries which separate one phase from another. Langton gave the corresponding phase boundary in the Neumann universe (a kind of phase space) the name edge of chaos. We should remember, however, that this ‘edge’ or boundary is not a sharp one. It is more like a thin or thick membrane in phase space, with chaotic behaviour on one side, and ordered behaviour on the other side of the membrane. There is a gradation from chaos to complexity to order across the membrane in phase space. And complex behaviour is at its most versatile within the membrane.

Many instances can be cited for the gradation from order to complexity to chaos. Even a simple computational algorithm like the Game of Life (mentioned in Section 11.3) is a universal computing device. The Game of Life is independent of the computer used for running it, and exists in the Neumann universe, just as other Class 4 CA do. As explained by Wolfram, such CA are capable of information processing and data storage etc. They are a mixture of coherence and chaos. They have enough stability to store information, and enough fluidity to transmit signals over arbitrary distances in the Neumann universe.

There are analogies of this, not only with computation, but with life, economies, and social systems also. After all, they are all just a series of computations. Life, for example, is nothing if it cannot process information. And life strikes a right balance between too static a behaviour and excessively chaotic or noisy behaviour.

14.5 Biological Complexity at the Edge of Chaos

The occurrence of complex phase-transition-like behaviour in the edge-of-chaos domain is something very common in practically all branches of human knowledge. Kauffman (1969), for example, recognized it in genetic regulatory networks (cf. Section 12.5 in Part 12). In his work on such networks in the 1960s, he discovered that if the connections were too sparse, the network would just settle down to a ‘dead’ configuration and stay there. If the connections were too dense, there was a chaotic churning around. Only for an optimum density of connections did the stable state cycles arise.

Similarly, in the mid-1980s, Farmer, Packard and Kauffman found in their autocatalytic-set model (Section 9.4) that when parameters such as the supply of ‘food’ molecules, and the catalytic strength of the chemical reactions etc., were chosen arbitrarily, nothing much happened. Only for an optimum range of these parameters did a ‘phase transition’ to autocatalytic behaviour set in quickly.

Many more examples can be given: Coevolutionary systems; economies; social systems; etc. A right balance of defence and combat in the coevolution of two species ensures the survival and propagation of both. Similarly, the health of economies and social systems can be ensured only by a right mix of feedbacks and regulation on the one hand, and plenty of flexibility and scope for creativity, innovation, and response to new conditions on the other. The dynamics of complexity around the edge of chaos is ideally suited for evolution that does not destroy self-organization.

But why and how do complex systems move towards the edge-of-chaos regime, and then manage to stay there? Per Bak supplied a clear and profound answer in terms of his important notion of self-organized criticality.

14.6 Self-Organized Criticality

Per Bak and coworkers (1996) argued that a particularly important consequence of self-organization (in a complex adaptive system) is the occurrence of self-organized criticality (SOC).

Let us consider a tabletop on which grains of sand are drizzling down steadily. To start with, the flat sandpile just grows thicker with time, and the sand grains remain close to where they land. A stage comes when the sand starts cascading down the sides of the table. The pile gets steeper and steeper with time, and there are more and more sandslides. With time the sandslides (avalanches or catastrophes) become bigger and bigger, and eventually some of the sandslides may span all or most of the pile. The average slope now becomes constant with time, and we speak of a stationary state.

This is a system far removed from equilibrium. Its behaviour has become collective. Falling of just one more grain on the pile may cause a huge avalanche (or it may not). The sandpile is then said to have reached a self-organized critical state. The edges and surfaces of the grains are interlocked in a very intricate pattern, and are just on the verge of giving way. Even the smallest perturbation can lead to a chain reaction (avalanche), which has no relationship to the smallness of the perturbation; the response is unpredictable, except in a statistical-average sense. The period between two avalanches is called a period of tranquillity (stasis) or ‘punctuated equilibrium.’

In a system in an SOC, big avalanches are rare, and small ones frequent. And all sizes are possible. There is power law behaviour: the average frequency of occurrence, N(s), of any particular size, s, of an avalanche is inversely proportional to some power τ of its size: N(s) = s^-τ. A log-log plot of this power-law equation gives a straight line, with a negative slope determined by the value of the exponent τ. The system is scale-invariant: Usually the same straight line holds for all values of s. Large catastrophic events (corresponding to large values of s) are consequences of the same dynamics which causes small events.

This is complex behaviour, and according to Bak, large avalanches, not gradual variation, can lead to qualitative changes of behaviour, and may form the basis for emergent phenomena and complexity. Bak (1996) gave several examples to make the point that Nature operates at the SOC state (or equivalently at the edge-of-chaos state). According to him, even biological evolution is an SOC phenomenon.

How do systems reach the SOC state, and then tend to stay there? The sandpile experiment provides an answer. Just like the constant input drizzle of sand in that system, a steady input of energy, or water, or electrons, can drive systems towards criticality, and then they self-organize into criticality by repeated spontaneous pullbacks from super-criticality, so that they are always poised at or near the edge between chaos and order.

We discussed beehives and ant colonies in Part 2. They are again nothing but examples of self-organization in open mutually-interacting systems. Many more examples of such ‘out of control’ complex adaptive systems exist.

14.7 Further Evolution of Complexity at the Edge of Chaos

The next question is: What do complex systems do when they have reached the edge of chaos? In the phase space of the dynamical system, the edge of chaos is a thin membrane, a region of complexity separating the ordered regime from the chaotic regime.

I introduced complex adaptive systems (CASs) formally in Part 5 of this series. John Holland (1998) pointed out the occurrence of ‘perpetual novelty‘ in a CAS, and said that this essentially amounts to saying that the system moves around in the edge-of-chaos membrane. But that is not all. The moving around can actually take the system to states of higher and higher sophistication of structure and complexity. Learning and evolution not only take a CAS towards the edge-of-chaos membrane in phase space, they also make it move within this membrane towards states of higher and higher complexity. The ultimate reason for this, of course, is that the universe is ever expanding, and there is therefore a perpetual input of free energy or negative entropy into it.

Farmer (1986) gave the example of the autocatalytic-set model (which he proposed along with Packard and Kauffman) to further illustrate the point about perpetual novelty and the ever-increasing degree of complexity of a CAS. When certain chemicals can collectively catalyze the formation of one another, their concentrations increase by a large factor spontaneously, far above the equilibrium values. This implies that the set of chemicals as a whole emerges as a new ‘individual’ in a far-from-equilibrium configuration. Such sets of chemicals can maintain and propagate themselves, in spite of the fact that there is no genetic code involved. In a set of experiments, Farmer and colleagues tested the autocatalytic model further by allowing occasionally for novel chemical reactions. Mostly such reactions caused the autocatalytic set to crash or fall apart, but the ones that crashed made way for a further evolutionary leap. New reaction pathways were triggered, and some variations got amplified and stabilized. Of course, the stability lasted only till the next crash. Thus a succession of autocatalytic metabolisms emerged. Apparently, each level of emergence through evolution and adaptation sets the stage for the next level of emergence and organization.

14.8 Concluding Remarks

It is often not realized by Darwinists and neo-Darwinists that natural selection alone cannot lead to such high levels of order and complexity as seen in living organisms. A high degree of order already exits in complex adaptive systems because of their self-organization and perpetual-novelty tendencies. Natural selection only hones this order to still higher levels of complexity.

The self-organization feature of complex adaptive systems may worry the Creationists some more. They have been busy attacking Darwin and his followers for the ‘blasphemies,’ and have been trying to argue that the fascinating degree of order observed in living creatures cannot possibly be the result of a series of ‘accidents’ in the form of mutations etc. The fact is that, as emphasized by Stuart Kauffman and others, Darwin or no Darwin, complex adaptive systems have the fundamental property that they self-organize into states of high (and ever-increasing) degree of order, so long as they are able to exchange matter and energy with the surroundings. Darwinian natural selection does lead to some increase of complexity and order but, by and large, it only hones the already available order and complexity to help a population adapt itself to the prevailing conditions.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

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COMPLEXITY EXPLAINED: 13. Evolution of Biological Complexity

Vinod K. Wadhawan — Mon, 25 Jan 2010 11:50:41 +0000

(Note: All previous parts in the Complexity Explained series by Dr. Vinod Wadhawan can be accessed through the ‘Related Posts’ listed below the article.)

In any evolutionary process, what evolves is complexity. Chemical complexity evolved till some of it became indistinguishable from biological complexity. Evolution of biological complexity is determined by two main factors: natural selection (made famous by Charles Darwin), and self-organization. I focus on the natural-selection aspect of biological evolution in this article.

13.1 Darwinian Evolution

The greatest single contribution to the subject of complexity was made (unwittingly, perhaps) by Charles Darwin. The year 2009 marked the second birth centenary of Darwin, as also 150 years of the publication of his celebrated book On the Origin of Species by Means of Natural Selection.

Living organisms are open systems, i.e. they are constantly exchanging matter and energy with the environment. There is a fair amount of dynamic equilibrium between a living organism and its surroundings. The organism cannot survive if this equilibrium is disturbed too much, or for too long. The fact that an organism survives implies that, in its present form, it has been able to adapt itself to the environment. If the environment changes slowly enough, living entities can evolve (over a long enough time period) a new set of capabilities or features which enable them to survive even under the changed conditions. Over long periods of such evolutionary change, creatures may even develop into new species. This was the message of Charles Darwin’s (1859) bold theory of evolution through cumulative natural selection. He demonstrated that adaptation to the environment was a necessary outcome of the exchange processes going on between organisms and their surroundings. A consequence of his theory was that all living organisms are the descendants of one or a few simple ancestral forms.

Darwin started with the observation that, given enough time, food, space, and safety from predators and disease etc., the size of the population of any species can increase in each generation. But this indefinite (exponential) increase does not actually occur, i.e. the so-called ‘biotic potential’ of a species is actually never realized. In fact, usually only a very tiny fraction of the biotic potential is realized, meaning that only a small minority of the offspring reach maturity to produce the next generation of offspring; the rest die prematurely.

Thus, there must be limiting factors in operation. Influenced by Malthusian ideas, Darwin imagined that if, for example, available food is limited, only a fraction of the population can survive and propagate itself. [Of course, it is now known that limited resources are seldom the primary factor influencing the course of evolution.] What decides who will survive and who will not?

Darwin’s answer was that, since not all individuals in a species are exactly alike (i.e. there is variation in the population), those which are better suited to cope with the prevailing conditions will stand a better chance of survival (survival of the fittest). The fittest individuals not only have a better chance of survival, they are also more likely to procreate. Thus, attributes conducive to survival get ‘naturally selected’ at the expense of less conducive attributes. And the effects of this natural selection accumulate over time. This is the process of cumulative natural selection recognized by Darwin.

It is also observed that children tend to resemble their parents to a substantial extent. The progeny of better-adapted individuals in each generation, which survive and leave behind more offspring than others, acquire more and more of those features which are suitable for good adaptation to the existing or changing environment. A species perfects itself, or adjusts itself, for the environment in which it must survive, through the processes of both cumulative natural selection and inheritance.

Thus there are four basic features of Darwinian evolution:

Variability and variety in members of a population in the matter of coping with a given environment.
Inheritance of this variation by the next generation, with random modifications.
Differential survival and reproductive success of individual members of this new generation in the given environment.
Establishment of a new population more adapted to the environment, possessing new variations to pass onto the next generation.

13.2 Neo-Darwinism

Darwin’s main postulate was that a species evolves because natural selection acts on small inheritable variations in the members of the species. But it was argued by his opponents that, since a species is also characterized by interbreeding among its members, such small variations should get averaged away. Darwin had no answer to counter this because the actual mechanism of inheritance was not known at that time. The answer in fact had been provided in 1865 (i.e. during the lifetime of Darwin, but apparently unknown to him) by the work of Gregor Mendel, the founder of the subject of genetics. We now know that the genotype or genome of an organism is its genetic blueprint. It is present in every cell of the body of the organism. The phenotype, on the other hand, is the end-product (the organism) which emerges through execution of the instructions carried by the genotype. It is the phenotype that is subjected to the battle for survival, but it is the genotype which carries the accumulated evolutionary benefits to succeeding generations. The phenotypes compete, and the fittest among them have a higher chance of exchanging genes among themselves.

Mendel’s laws of genetics were rediscovered independently by quite a few workers. One of them was the Dutch botanist Hugo de Vries, who not only rediscovered Mendel’s laws for the inheritance of dominant and recessive characteristics, but also genetic mutations. These were sudden (unexplained) changes of form which could be inherited by the offspring.

The present, post-Darwinian picture is that the inherited characteristics of the progeny are caused by genes. In sexually reproducing organisms, each parent provides one complete set of genes to the offspring. Genes are portions of molecules of DNA, and their specificity is governed by the sequences in which their four bases (adenine (A), thymine (T), guanine (G), and cytosine (C)) are arranged. The double-helix structure of DNA, together with the restriction on the pairing of bases comprising the DNA molecule to only A-T and G-C, provides a mechanism for the exact replication of DNA molecules. The DNA sequence on a gene determines the sequence of amino acids in the specific proteins created by the live organism.

Genes programme embryos to develop into adults with certain characteristics, and these characteristics are not entirely identical among the individuals in the population. Genes of individuals with characteristics that enable them to reproduce successfully tend to survive in the gene pool, at the expense of genes that tend to fail. This feature of natural selection at the gene level has consequences which become manifest at the organism or phenotype level. Cumulative natural selection is not a random process.

If like begets like (through inheritance of characteristics), by what mechanism do slight differences arise in the gene pool of successive generations so that the species evolves towards evolutionary novelty? Apart from crossover (where applicable), one mechanism is that of mutations. Mutations, brought about by radiation or by chemicals in the environment, or by any other agents causing replication errors, change the sequence of bases in the DNA molecules comprising the genes.

In organisms in which sexual reproduction has been adopted as the means for procreation, since the genetic material has two sources instead of one (namely, the two parents), the occurrence of variations in the offspring is higher. The genes from the parents are reshuffled in each new generation. This increases the evolutionary plasticity of the species. However, not all differences in individuals in a population are due to the genetic makeup. Factors such as nutrition also play a role. The observable characteristics of an organism, i.e. its phenotype, are determined both by the genetic potentiality and by the environment.

If all living beings have the same or only a few ancestors, how have the various species arisen? The Darwinistic answer lies in isolation and branching, aided by evolution. Migrations of populations also play a role in the evolutionary development of species. If there are barriers to interbreeding, geographical or otherwise, single populations can branch and evolve into distinct species over long enough periods of time. Each such branching event is a speciation: A population accidentally separates into two, and they evolve independently. When separate evolution has reached a stage that no interbreeding is possible even when there is no longer any geographical or other barrier, a new species is said to have originated.

The term neo-Darwinism essentially connotes a modification of the original ideas of Darwin in the light of later knowledge about the mechanism of transmittal of genetic information from one generation to the next. Margulis and Sagan (2003), who disagree with this neo-Darwinistic view of the origin of species, have summed up neo-Darwinism as follows (in their book Acquiring Genomes: A Theory of the Origins of Species): ‘All organisms derive from common ancestors by natural selection. Random mutations (heritable changes) appear in the genes, the DNA of organisms, and the best “mutants” (individuals bearing the mutations) in competition with the others, are naturally selected to survive and persist. The unsuited offspring die - they tend to be called “unfit” - with fitness, a technical term, referring to the relative numbers of offspring left by an individual to the next generation. The most fit, by definition, produce the largest number of offspring. The mutant variations then leave more offspring, and populations evolve; that is, they change through time. When the number of changes in the offspring accumulates to recognizable proportions, in geographically isolated populations, new species gradually emerge. When sufficient numbers of changes in offspring populations accumulate, higher (more inclusive) taxa gradually appear. Over geological periods of time new species and higher taxa (genera, families, orders, classes, phyla, and so on) are easily distinguished from their ancestors.’

13.3 Lamarckism

When Darwin published his theory of biological evolution, the field of genetics had not yet taken shape. Naturally, Lamarck, whose work on biological evolution preceded that of Darwin, was also unaware of the crucial role of the genetic mechanism in the evolution of species. We now have the familiar concepts of genotype and phenotype. The term genotype (or genome) refers to the genetic constitution of an organism. It is the genetic blueprint encoded in its strings of DNA chains. Phenotype, on the other hand, signifies the characteristics manifested by an organism; it is the structure created by the organism from the instructions in its genotype. The phenotype is the organism itself. Genotypes correspond to the ’search space,’ and phenotypes to the ’solution space.’

Biological evolution is generally believed to be all Darwinian. Namely, variety in a population means that some individuals have a slight evolutionary advantage with respect to a particular characteristic, which is therefore more likely to be passed on to the next generation if it helps in the survival and propagation of the species. Over time, this characteristic gets strengthened and is easily noticeable in the phenotype. Lamarck’s theory of evolution, or Lamarckism, on the other hand, was based on two premises: the principle of use and disuse; and the principle of inheritance of acquired characteristics (without involving the genotype).

But can the environmental conditions in which the parents live indeed affect the genetic characteristics of the offspring? ‘No’ according to the neo-Darwinian theory of evolution, and ‘Yes’ according to the theory of Lamarck. The Lamarckian viewpoint of inheritance of acquired characteristics is not acceptable in modern biology because it runs counter to the central dogma of modern molecular genetics, according to which information can flow from DNA to proteins (or from genotype to phenotype), but not from proteins to DNA (or from phenotype to genotype).

Although Lamarckism is unacceptable for explaining biological evolution, nothing prevents us from using it in artificial evolution (i.e. inside a computer) and exploiting the much higher speed it may offer for reaching an end-goal.

13.4 Epigenetics

The new (or rather currently hotting up) field of research called epigenetics has brought us dangerously close to Lamarckism, without violating the central dogma of molecular biology. It is now clear that changes other than those in the sequence of nucleic acids in DNA, acquired during the lifetime of parents or grand-parents, can indeed be inherited. Gene expression or interpretation can be influenced by molecules hitchhiking on genes. This heritable non-genetic hitchhiking is called epigenetic inheritance.

The DNA in the cell of an organism carries the genetic blueprint for the synthesis of various proteins needed by the organism. Some of these proteins even help in the synthesis of other proteins via the instructions embodied in the nucleotide sequence along the DNA chain. But there is no way proteins by themselves can engineer the synthesis of the nucleic acids comprising the DNA. Thence the central dogma of molecular biology that information can flow from nucleic acids to proteins, but not from proteins to nucleic acids. Lamarckism in its original form is unacceptable in the theory of biological evolution because it implies that the information acquired by an organism during its life time, and therefore incorporated in its phenotype only (e.g. as proteins), can be somehow transmitted to the offspring via the genotype.

But there is a ‘loophole’ in this argument! Genes, which are nothing but portions of the long sequence of nucleic acids in the DNA chain, contain the coded information for synthesizing various proteins. And we have known since the days of Mendel that not all genes are active all the time. Some genes are dominant, while others are recessive. One of the Mendelian laws of genetics is that it is the combination of dominant and recessive genes inherited from the parents that dictates the characteristics (phenotype) of an offspring. The dominant genes are the active or ’switched on’ genes, and recessive genes are the ’switched off’ genes. We saw in Section 12.3 (Part 12) how the presence of certain hormones can influence gene expression, and once a gene has been switched on by the presence of a hormone, it acts as a switch which can alter the ‘on’ or ‘off’ states of other genes. So hormones are one example of what can influence gene activity.

But hormones, after all, are nothing but chemical compounds. Can other chemicals, for example those in the diet of an organism, also influence gene expression? The answer is ‘Yes’. And not just food, but even the mental state of an animal can be responsible for the secretion of chemicals which can influence gene expression. From the point of view of genetics and transmittal of acquired characteristics, the influence on the genotype of a parent should be of an irreversible nature; only then can it affect the progeny in a permanent manner; then only can we have an inheritance of acquired characteristics by the progeny. This is the subject matter of the field of epigenetics.

Epigenetics is the study of changes in gene function that do not depend on changes in the primary DNA sequence, and depend on stable, heritable marking of DNA. Epigenetic effects influence the phenotype, without changing the genotype.

One particular heritable marking of DNA that has been investigated substantially is that of methylation, i.e. attachment of the -CH₃ group to one or more nucleic acids along the DNA chain. It has been found, for example, that methylation is quite frequent in cancer cells and it is difficult to distinguish from mutations. Methylation affects gene expression, and, as a feedforward mechanism, can have serious transgenerational effects. Epigenetic changes can be passed through the germ line for many generations. And epigenetic changes can occur throughout the life time of an individual: Methylation can turn some genes off, and demethylation can turn other genes on.

The Human Genome Project was completed a few years ago. It has mapped out the sequence of all the three billion nucleotide pairs comprising the human DNA. And the Human Epigenome Project has been already started. Its goal is to add an indicator to every spot on the DNA where methyl markers can attach and change the gene expression there.

13.5 Theories of the Origins of Species

Ironically the popular evolutionist’s view that organisms evolve by the accumulation of random mutation best describes the evolutionary process in bacteria. All of the larger, more familiar organisms originated by symbiont integration that led to permanent associations.

Margulis and Sagan (2002)

Indeed, as Wallin wrote in 1927, ‘It is a rather startling proposal that bacteria, the organisms which are popularly associated with disease, may represent the fundamental causative factor in the origins of species.’ We agree.

Margulis and Sagan (2002)

The classical viewpoint that speciation occurs, i.e. new species arise, as a result of the cumulative effect of mutations, has been strongly contested by Lynn Margulis. According to Margulis and Sagan (2002), ‘No evidence in the vast literature of heredity change shows unambiguous evidence that random mutation itself, even with geographical isolation of populations, leads to speciation. Then how do new species come into being? How do cauliflowers descend from tiny, Mediterranean cabbagelike plants, or pigs from wild boars?’ Their answer is that species arise largely by the acquisition of entire genomes through symbiogenesis.

Margulis’s stance has raised debate. Ernst Mayr wrote an appreciative foreword to the book by Margulis and Sagan (2002). But the foreword also said this: ‘Speciation - the multiplication of species - and symbiogenesis are two independent, superimposed processes. There is no indication that any of the 10,000 species of birds or the 4,500 species of mammals originated by symbiogenesis.’ Contrast this with the statement of Rachel Nowak (2005): ‘Symbiosis has popped up so frequently during evolution that it is safe to say that it’s the rule, not the exception.’

Life appeared on Earth during what F. Niele (2005) calls the thermophilic regime of energy in the history of the Earth. This form of life comprised of microorganisms that thrived in hot conditions. Chemical evolution and diversification of molecular structure had occurred, and closed-loop autocatalytic reactions had led to the creation of life-like molecules of increasing complexity. Things progressed to a point where the forebears of DNA started appearing, which had potential for replication. The biological prokaryotic cell emerged in due course. This energy regime also saw the emergence and establishment of a metabolism mechanism for the supply of energy, with ATP as the principal ‘cellular energy currency.’ The living organisms of this period used nucleotides for synthesising DNA, and amino acids for synthesising proteins. There was practically no free oxygen in the atmosphere of the Earth.

The next energy regime, namely the phototrophic regime, was dominated by the exploitation of solar energy. It came about because some of the living organisms of the thermophilic regime reached the surface of the sea, where they encountered sunlight. In due course they developed a new metabolism which used solar energy through photosynthesis. Fixing of carbon dioxide, as also the stripping of hydrogen from water (resulting in the creation of free oxygen), originated in this energy regime. The new microorganisms which achieved this were cyanobacteria or blue-greens. They stripped electrons from water molecules, thus releasing hydrogen for use, along with carbon dioxide, in the production of carbohydrates. This gradually built up the molecular-oxygen content of the Earth. Within a few hundred thousand years the atmospheric oxygen levels rose from less than 1% to ~15% of present-day levels.

Abundant availability of solar light made the population of the blue-greens to grow, producing more and more oxygen. But oxygen itself was poison to them. Therefore evolutionary adaptation led to the development of a new kind of cell, namely the eukaryotic cell, which had ‘organelles’ limited by membranes. Let us see how this resolved the crisis.

The atmospheric oxygen in the phototrophic regime was conducive to the aerobes, but poison for the anaerobic blue-greens. However, the blue-greens did not simply fade away in such a situation, as they were instrumental, not only in the production of molecular oxygen, but also in the production of food for the respiring aerobes. Therefore the build-up of oxygen in the atmosphere was really a threat to both these types of organisms. They responded by evolving a symbiogenesis of the two, resulting in the emergence of the eukaryotic cell. Such a cell has an outer membrane which protects its contents from the harsh conditions outside. It also has internal membranes housing the organelles. Lynn Margulis pointed out that the organelles called mitochondria of a green plant cell are descended from an oxygen-respiring bacterium. Similarly, the chloroplasts are descended from another free-living bacterium, namely a cyanobacterium. The first aerobic eukaryotes had the enzymatic tools to detoxify reactive oxygen products. This is how they could ensure the survival of the symbiont blue-greens. In a photosynthesizing eukaryotic cell, the chloroplasts store solar energy in sugars and supply it to the host. The host, in turn, supplies the sugar to the mitochondria, which then supply the host with ATP, the cell fuel.

This symbiogenesis between oxygenic photosynthesis and aerobic respiration was at the heart of the oxo-energy revolution (Niele 2005), resulting in the emergence of the aerobic energy regime. The emergence of the eukaryotic cell embodied sunlight-harvesting photosynthesis, and protection against oxygen toxicity. Its highly efficient metabolic combustion via aerobic respiration triggered the appearance of multicellular life forms which, in turn, led to the emergence of still more complex life forms and ecosystems. Humans appeared on the scene in due course.

Of course, the eukaryotic organisms have continued to coexist with the prokaryotic organisms (namely the bacteria and the archaea) in several schemes. In fact, as Knoll said, the prokaryotes ‘maintain the foundation of all functioning ecosystems on this planet.’ An example is the nitrogen that bacteria make available for biological processes.

Emergence of new species

Life originated with bacteria. Bacteria do not speciate the way eukaryotic organisms do. The idea of a species does not apply to them. Bacteria can pass genes back and forth. There is no fixed genome to define the species of any bacteria. Bacteria are prokaryotes.

The first eukaryote emerged by the symbiogenesis of two prokaryotes. The concept of a species can apply only to eukaryotes. It follows that the origin of species occurred long after the origin of life in the form of bacteria. Therefore, the species of all the larger organisms (protoctists, fungi, animals, plants) originated symbiogenetically in the beginning. Nucleated organisms emerged on Earth some 1200 million years ago.

However, there is no reason to believe that symbiogenesis is the only way in which new species can arise. It is characteristic of complex systems that, often, small changes can have unexpectedly large consequences, including the emergence of new species. Effects of mutations can gradually build up to a stage wherein a sudden bifurcation occurs in phase space, and a new species arises. Speciation may well be an emergent phenomenon. This is in disagreement with the statement of Margulis and Sagan (2002) that ‘intraspecific variation never seems to lead, by itself, to new species.’

13.6 Concluding Remarks

Any entities that can replicate, and that have a variation both in their specific features and in their reproductive success, are candidates for Darwinian selection and evolution.

Darwin changed the way we humans look at ourselves and our place in the world. The basic idea of evolution by natural selection has gone far beyond the precincts of biology, and has permeated the human psyche in all sorts of ways. Apart from biological Darwinism, we speak of chemical Darwinism, quantum Darwinism, neural Darwinism, and what not. Deep down under, what evolves in any system is complexity.

Evolution of biological complexity is determined by two factors: natural selection, and self-organization. Self-organization creates order in any complex system. Darwinian natural selection acts on this existing order and hones it further. I shall dwell on the self-organization aspect of biological complexity in the next article in this series. Symbiogenesis is not the only way in which new species can arise. Often, small changes in complex adaptive systems can have unexpectedly large consequences, including the emergence of new species. Effects of mutations can gradually build up to a stage wherein a sudden bifurcation occurs and a new species arises. Speciation may well be an emergent phenomenon in a complex adaptive system.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS

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COMPLEXITY EXPLAINED: 12. The Likely Origins of Life

Vinod K. Wadhawan — Sat, 26 Dec 2009 00:27:45 +0000

(Note: This is Part 12 of Dr. Wadhawan’s series on Complexity. All previous parts of the series can be accessed through the Related Posts list at the bottom of this article.)

According to one model of the origins of life, it is likely that life originated twice, with two separate kinds of organisms, one capable of metabolism without exact replication, and the other capable of replication without metabolism; at some stage the two features came together. Another model is that life originated with the emergence of RNA molecules which could act as both enzymes and self-replicators. In either case, the emergence of self-replicators also marked the first step towards the evolution of consciousness.

12.1 Freeman Dyson’s Dual-Origin Model for Life

Freeman John Dyson is a theoretical physicist and mathematician, well known for his work in quantum field theory, solid-state physics, and nuclear engineering. In 1949 he demonstrated the equivalence of the two formulations of quantum electrodynamics, one by Richard Feynman and the other by Julian Schwinger and Sin-Itiro Tomonaga. In 1985 he wrote a little book Origins of Life, in which he argued that metabolic reproduction and replication are logically separable propositions, and that natural selection does not require replication, at least for simple creatures. In higher-level life as seen today, reproduction of cells and replication of molecules occur together. But there is no reason to presume that this was always the case. According to Dyson, it is more likely that life originated twice, with two separate kinds of organisms, one capable of metabolism without exact replication, and the other capable of replication without metabolism. At some stage the two features came together. When replication and metabolism occurred in the same creature, natural selection as an agent for novelty became more vigorous.

Dyson acknowledged the influence of Erwin Schrödinger and John von Neumann on his work. Two other scientists whose work he used for proposing his dual-origin hypothesis for life were the chemists Manfred Eigen and Leslie Orgel. They had demonstrated that a solution of nucleotide monomers will, under suitable conditions in the laboratory, give rise to a nucleic-acid polymer molecule (RNA) which replicates and mutates and competes with its progeny for survival. For achieving this, Eigen used a polymerase enzyme, which was a protein catalyst extracted from a bacteriophage (the synthesis and replication of the RNA depends on the structural guidance provided by the enzyme). Orgel did something complementary to the experiment of Eigen. He made RNA grow out of nucleotide monomers by adding a template for the monomers to copy, but did not add a polymerase enzyme. Thus Eigen made RNA using an enzyme but no template, and Orgel made RNA using a template but no enzyme. Living cells use both templates and enzymes for making RNA. This pointed to a possible parasitic development of RNA-based life in an environment created by a pre-existing protein-based life.

Dyson also drew support and inspiration from the work of Lynn Margulis, who has been a major proponent of the idea that parasitism and symbiosis were the driving forces in the evolution of cellular complexity. [Symbiosis means a prolonged living arrangement or physical association among members of two or more different species. Levels of partner integration in symbiosis may vary in intimacy; and integration may be behavioural, metabolic, of gene products, or 'genic.']

Margulis has been hammering home the point that the main components of eukaryotic cells have descended from independent living creatures which ‘attacked’ the cells from outside. In due course, the attackers and the host evolved a relationship of mutual dependence and benefit. In stages, the erstwhile invading organisms became first chronic parasites, then symbiotic partners, and finally an indispensable part of the host. The evidence for this is that the molecular structures of mitochondria and chloroplasts are indeed very close to certain bacteria.

Margulis has marshalled evidence to argue that most of the big steps in cellular evolution were caused by parasites. And the nucleic acids were the oldest and the most successful cellular parasites. According to Dyson’s model, the original living creatures were cells with a metabolic apparatus directed by certain proteins (enzymes), which had no genetic appurtenances to start with. Such cells lacked the ability for exact replication, but could still grow and divide and reproduce themselves in an average statistical manner.

12.2 ATP and RNA

During millions of years of chemical (and now also biological) evolution, the initial primitive but living cells diversified and refined their metabolic reaction pathways. In particular, they evolved the synthesis of ATP (adenosine triphosphate) through some autocatalytic reaction mechanisms (cf. Part 9). ATP is the main energy-carrying molecule in all present-day cells. ATP-carrying primitive cells had an evolutionary advantage over other, less efficient, cells. In time, other molecules like AMP (adenosine monophosphate) emerged; or perhaps AMP came first, and then ATP.

The molecular structures of Adenosine triphosphate (ATP) and Adenosine 5'-monophosphate (AMP), otherwise known as adenine nucleotide.

Now, although ATP and AMP have similar chemical structures (see figure), they play totally different roles in present-day cells. ATP is the universal biological currency for energy. AMP, on the other hand, is one of thenucleotides in the structure of the RNA molecule. RNA functions as the carrier of information, and it can replicate exactly. [RNA is like DNA, except that thymine (T) is replaced by uracil (U). In RNA, A bonds to U only, and G bonds to C only.] AMP is the A (i.e. the nucleotide adenine) in the RNA structure.

If ATP loses two of its three phosphate groups, it becomes AMP. Dyson argued that, although the primitive cells had no genetic apparatus to begin with, they were loaded with ATP molecules which could easily convert to AMP molecules. Accidentally, in one such cell which happened to be carrying AMP and other nucleotides (the ‘chemical cousins’ of AMP), the Eigen experiment for synthesizing RNA happened spontaneously. With some help from pre-existing enzymes, an RNA molecule got produced. Once created, it went on replicating itself because of the proclivity of base A to hydrogen-bond with base U, and of G to hydrogen-bond with C.

Thus, RNA first appeared as a parasitic disease in the cell. Although most such cells died of disease, some evolved to survive the infection, à la Lynn Margulis. In such cells, the parasite gradually became a symbiont. Further evolution resulted in a situation in which the protein-based life learnt to make use of the ability for exact replication provided by the chemical structure of RNA. This is how the modern genetic mechanism came into being. Hardware came before software, and that makes sense.

Is it really true that proteins emerged before RNA? The early evidence came from laboratory experiments done during the 1950s. The well-known experiments by Miller and others (done from 1953 onwards) demonstrated that amino acids form easily in a reducing atmosphere from the still simpler molecules, in the presence of ultraviolet radiation. What about nucleotides?

They are more difficult to synthesize from their constituents in a Miller-style experiment. A nucleotide has three parts: an organic base, a sugar, and a phosphate ion. The phosphate ion occurs naturally as a constituent of rocks and sea water. The sugar part can be synthesized with substantial efficiency from formaldehyde. And the synthesis of an organic base was demonstrated by Oró in 1960. He prepared a concentrated solution of ammonium cyanide in water, and just let it stand. Adenine was self-created, with a 0.5% yield. Guanine also got synthesized in a similar way. But the catch here is that it is difficult to imagine how such high degrees of concentration of ammonium cyanide could occur in Nature, although some possible scenarios have been suggested. In any case, the nucleotide molecules, even if formed, are unstable in solution, and tend to get hydrolysed back into their components. Another major difficulty is to get the three components of a nucleotide into a correct configuration for bonding.

All told, whereas it is easy to simulate a pre-biotic synthesis of amino acids in the laboratory, the same is not the case for nucleotides (but see below). Dyson argued that this lends credence to his model that proteins appeared on the scene before RNA etc. Of course, he was also quick to point out that perhaps we have not been clever enough to create proper simulation conditions in the laboratory. I shall return to this point in Section 12.6.

12.3 How the Mystery of Cell Differentiation was Solved

For introducing certain concepts and terminology, I make a small digression here and discuss cell differentiation. Each cell of our body carries the same genome. What tells some cells to become kidney cells, and others to become liver cells, and still others to become neurons? The term ‘cell differentiation’ is used for this phenomenon. How does cell differentiation occur, and with such high precision?

French scientists François Jacob and Jacques Monod were awarded (along with Andre Lwoff) the Nobel Prize for physiology or medicine for 1965 for their work on ‘genetic circuits.’ There are thousands of genes arrayed along a DNA molecule. Jacob and Monod discovered that a small fraction of these are ‘regulatory’ genes which can function as switches. Such activity is triggered by, say, the availability of a particular hormone in the surroundings of a cell. This hormone may switch-on a particular gene. The newly activated gene sends out chemical signals to fellow genes, that can switch them on or off, depending on the states they are already in. The altered state of each of these genes then releases, or stops releasing, other chemical signals, which are received by the genetic switches in the network, altering their states in turn, in a cascading manner. This continues till the network of genetic switches settles down to a stable, self-consistent pattern.

From left to right: François Jacob, Jacques Monod and André Lwoff, who were awarded the Nobel Prize for Physiology of Medicine in 1965. Jacques Monod was director of the Pasteur Institute in Paris from 1971 to 1976.

This work had several implications. For example, it established DNA as not just a repository of the blueprint for the cell, telling it how to manufacture the various proteins, but also as an engineer in charge of construction. The DNA was established to be a molecular-scale computer that computed how the cell was to build and repair itself, and how it was to interact with the surrounding world.

The work of Jacob and Monod also solved the mystery of cell differentiation. It was concluded from this work that each type of cell corresponds to a different pattern of the genetic network, influenced by the presence of specific hormones etc. Although there is only a single genome involved, the genome can have many stable patterns of activation, each corresponding to a different cell type (liver, kidney, brain, etc.). Thus the genome was viewed as a complex network of interacting components, which control homeostasis and differentiation through very specific control circuits among the genes. [Homeostasis is the ability of higher animals to maintain an internal consistency.]

Back to Dyson. Further support for his dual-origin model for life has come from the work of Stuart Kauffman who carried forward the regulatory-genetic-networks idea. Before describing this, I must introduce the important idea of attractors in phase space. Sorry about the digression; I had vowed not to use any unexplained jargon in this series of articles.

12.4 Attractors in Phase Space

The concept of phase space or state space was introduced in Section 6.2 (Part 6) of this series. Imagine a loosely wound spring, oriented vertically (i.e. along the z-axis), and fixed securely at its top end to some heavy object. At its bottom end I attach a small particle. I am interested in the dynamics of this particle after I pull the lower end of the spring by a small distance, and then release the spring. The spring will be set into vibration, and the attached particle will execute an oscillatory, up-and-down motion. At any instant of time, the particle has position coordinates (x, y, z), and momentum coordinates (p_x, p_y, p_z). What is the phase-space trajectory for this system? The answer is that it is a closed loop in a plane defined by the z-axis and the p_z-axis. Let us see how.

To start with (i.e. at time t = 0), the particle attached to the spring is at rest, and its representative point in phase space has the ‘coordinates’ (0, 0, 0, 0, 0, 0). At the moment I release the spring after pulling it by a small distance z, the phase-space coordinates are (0, 0, -z, 0, 0, 0).

When I was pulling the spring, I was doing work against its restorative force, and this work got stored as the potential energy of the spring. When I release the spring, this stored potential energy is available for doing work, making the spring (and the particle attached to it) move towards the initial position (0, 0, 0) of the particle in real space. By the time the particle reaches this point, all the potential energy has got converted to kinetic energy, and the representative point in phase space now has the coordinates (0, 0, 0, 0, 0, p_z). Nothing much is happening along the x-axis and the y-axis, as also along the p_x-axis and the p_y-axis. All the action is along the z-axis and the p_z-axis, so we can use a more compact notation, and say that at the moment when all the potential energy has got converted to kinetic energy, the representative point in phase space has the coordinates (0, p_z).

The kinetic energy of the spring will make the particle overshoot the origin point of the z-axis till the particle reaches the representative point (z, 0); this is when the particle will be at rest again, as all the kinetic energy has been converted back to potential energy. This potential energy will again make the particle move in the opposite direction. And so on. Thus the particle will successively and repeatedly pass through a whole continuum of points in phase space, including the following points: (-z, 0), (0, p_z), (z, 0), (0, -p_z).

If there is no dissipation of energy, the phase-space trajectory in this experiment is a closed loop, as the particle repeatedly passes through all the allowed (i.e. energy-conserving) position-momentum combinations again and again. Since the trajectory is fixed or constant, the area enclosed by it is also constant.

But in reality, dissipative forces like friction are always present, and in due course all the energy I expended in stretching the spring will be dissipated as heat. What happens to the phase-space trajectory of the particle as the total energy (potential energy plus kinetic energy) is lost gradually? As the total energy decreases, the maximum value of the z-coordinate during the trajectory cycle, as also the maximum value of p_z, will also decrease, implying that the area enclosed by the trajectory in phase space will decrease, till the particle finally comes to a state of rest or zero momentum.

This final configuration corresponds to an attractor in phase space: It is as if the dissipative dynamics of the system is ‘attracted’ by the point (0, 0, 0, 0, 0, 0) as its energy gets dissipated. Thus, because of the gradual dissipation of energy, the closed-loop phase-space trajectory spirals towards a state of zero area. This is like a particle set rolling in a bowl, spiralling towards the bottom of the bowl; the bowl thus acts as a basin of attraction. Similarly, the phase-space region around the attractor (0, 0, 0, 0, 0, 0) is the basin of attraction for the oscillator problem we have considered here. I had pulled the spring by an arbitrary small amount. The exact magnitude of this small amount of pulling is not important. In each such experiment (with different starting values of z), the dissipative system always gets attracted towards the same attractor. We say that there is a unique basin of attraction around the unique attractor.

Nonlinear Dynamical Systems

In the above experiment, if we pull the spring only by a small amount, its restorative force is linearly proportional to the displacement of the tip of the spring to which we have attached the small particle: If we plot this force f_z as a function of z, we get a straight line (which is a linear curve). Incidentally, this is the defining feature of what is called a simple-harmonic oscillator.

But if the displacement is too large, the restorative force is no longer linearly proportional to the displacement z, and we are then dealing with a nonlinear dynamical system: The plot of f_z against z is no longer a straight line. Most real-life phenomena involve nonlinear dynamics. In particular, all evolution of complexity in Nature concerns systems which receive a persistent and therefore cumulatively large amount of energy from the surroundings, and are thus pushed into the nonlinear regimes of dynamic behaviour. All complex systems are nonlinear, although not all nonlinear systems may exhibit complex behaviour.

12.5 Kauffman’s Work on the Origins of Life

In 1993 Kauffman had established by his cellular-automata approach that regulatory genetic networks can indeed arise spontaneously in complex systems by self-organization. But he still had to tackle the question of how extremely large molecules like RNA and DNA came into existence in the first place. In any case, as stated earlier in this series of articles, even DNA requires the availability of certain protein molecules for its genetic role. Therefore, there must have been a mechanism which resulted in the spontaneous creation of protein molecules without the intervention of DNA.

In other words, there must have been a non-random origin of life. There must have been another way, independent of the need to involve DNA molecules, for self-reproducing molecular systems to have got started. Kauffman carried Melvin Calvin’s (1969) idea of autocatalytic reactions (cf. Part 9) much further to explain how this could happen: In Kauffman’s model, like in Dyson’s, life originated before the advent of RNA or DNA. And Kauffman’s network model could incorporate features like reproduction, as also competition and cooperation for survival and evolution (including coevolution). Kauffman had introduced in 1969 his ‘random Boolean networks’ (RBNs) as a part of his pioneering work on the functioning of genetic regulatory networks. He went a step further than Jacob and Monod and demonstrated that even randomly constructed networks of high molecular specificity can undergo homeostasis and differentiation:

In the absence of knowledge regarding the parameters describing real cells, Kauffman investigated (on a computer) a variety of genetic control networks to see if any of them simulated biological activity reasonably well. In his binary network model (or the RBN model), a gene (represented as a node of the network) was modelled as a binary device, the whole network having N such nodes. Thus, each node or gene had two possible states: ‘on’ or 1, and ‘off’ or 0. The ‘on’ state meant that the gene was being transcribed, and the ‘off’ state meant that it was not being transcribed. Each gene or node was modelled as receiving exactly K (K less than or equal toN) inputs from randomly chosen ‘controlling’ genes or nodes, and also receiving one random ‘update’ function for its K inputs. The update function prescribes the state of the gene or the automaton in the next time step, given its state in the current time step, and is chosen according to some probability-distribution function. By varying N and K for these RBNs, the behaviour of a variety of such finite sequential switching automata could be investigated. At any time step, each gene or node had a value 1 or 0, and the network was a collection of these 1s and 0s, representing the ’state’ of the network or the biological cell. This pattern of 1s and 0s served as the input, determining the pattern for the next time step of the automaton. Shown in the adjoining figure is the activity pattern for an RBN with 16 nodes for 50 time steps. The initial state is the column furthest to the left with nodes represented vertically and time moving to the right.

The RBN has 2^N possible states; i.e. it has a finite number of states. This finiteness, coupled with the fact that the dynamics is det erministic, implies that, as the RBN proceeds through a sequence of states, it must eventually return to a pattern it had at some earlier time step, and from then on it must repeat the same pattern-sequence periodically. That is, it must be trapped in a re-entrant cycle of states, or an attractor in phase space. Each such state cycle or attractor represents a distinct temporal mode of behaviour of the net, and was equated by Kauffman with a distinct cell type (kidney, liver, etc.). Cell types differ only in the pattern of gene activity; they all carry the same genome. Shown in the adjoining figure is a periodic attractor (yellow) and its basin of attraction (cyan). Each point in the state space represents a network state.

Kauffman focussed his attention on ‘critical‘ RBNs. These lie at the ‘edge of chaos,’ i.e. at the boundary between frozen networks and chaotic networks. Frozen networks have very short attractors or cycle lengths. And chaotic networks have large-sized attractors that may include a substantial portion of the phase space. To quote Kauffman:

Let’s talk about networks as a model of the genetic regulatory system. My claim is that sparsely connected networks in the ordered regime, but not too far from the edge (of chaos) do a pretty good job of fitting lots of features about real embryonic development, and real cell types, and real cell differentiation. And insofar as that’s true, then it is a good guess that a billion years of evolution has in fact tuned real cell types to be near the edge of chaos. So that’s very powerful evidence that there must be something good about the edge of chaos. So let’s say the phase transition is the place to be for complex computation. Then the second assertion is something like ‘Mutation and selection will get you there.’

[The edge-of-chaos idea is very important for understanding complexity and the origin and sustenance of life, and I shall discuss it in some detail in a separate article.]

Jacob and Monod’s cell types, distinguished from one another by the distinct and stable network patterns of gene activity, were interpreted by Kauffman as represented by different attractors in phase space. For K = 1and for K = Nthe length of the attractor cycles is very large. But for K = 2, i.e. when there are two inputs per gene, the lengths of the cycles are very small, roughly scaling as ~√Nfor critical networks. For example, for N = 1000, i.e. for 2¹⁰⁰⁰ possible states of the network, the modelled genome was found to cycle typically among just 30 time steps, a remarkable result indeed. Kauffman also found that the number of cell types scales as √N, in line with the biological information available at that time.

Thus Kauffman demonstrated that highly ordered dynamical behaviour is typical even for randomly constructed genetic networks getting just a few inputs per component. This implied that homeostasis in living complex systems is a direct consequence of the high molecular specificity among the macromolecules involved. Similarly, cell differentiation reflects the capacity of complex adaptive systems to behave in several distinct, highly localized ways. Kauffman’s work established that complex genetic networks could come into being by spontaneous self-organization, without the need for slow evolution by trial and error. After all, the whole thing had to be there together, and not partially, to function at all. He also established that genetic regulatory networks are no different from neural networks.

Kauffman’s work, though extremely important and path-breaking, was handicapped by the limited computational power available at that time, as also the limited nature of biological data. We now know that the number of genes (N) is not proportional to the mass of DNA, contrary to what was assumed by biologists at that time; it is much smaller for higher organisms. And that, for larger N, the increase in the number of genes with N is much faster than √N. In fact, the attractor number, as also the attractor length of K = 2networks, both increase with the size of the network faster than any power law.

12.6 Freeman Dyson Revisited

I summarize here an updated version of Dyson’s ideas, as given in the recent (2008) book Life: What a Concept! In his model, there are six stages in the evolution of chemical complexity, leading to the emergence of life as we see it today.

Stage 1. The early cells were just little bags of some kind of cell membrane; this is the ‘garbage bag model’ for Stage 1. And inside the bag there was a more or less random collection of organic molecules, with the characteristic that small molecules could diffuse in through the membrane, but big molecules could not diffuse out. The ‘garbage bag’ situation was conducive to the conversion of small molecules into large molecules. And the higher concentration of organic material in the bag led to a higher efficiency of the chemical processes involved. This was conducive to fairly rapid evolution of chemical complexity.

And this evolution did not involve any replication processes. ‘When a cell became so big that it got cut in half, or shaken in half, by some rainstorm or environmental disturbance, it would then produce two cells which would be its daughters, which would inherit, more or less, but only statistically, the chemical machinery inside. Evolution could work under those conditions. In Stage 1, evolution was happening, but only on a statistical basis. This was pre-Darwinian evolution.’

Stage 2. Parasitic RNA appeared in some of the cells in Stage 2. ATP had appeared in one of the garbage bags by a random process in Stage 1, and the cell hosting it had a metabolic advantage over other cells. Therefore many cells with large amounts of ATP got created. Then, again by chance, ATP changed to AMP in one of the cells, and AMP is nothing but the adenine nucleotide. In due course, AMP and its chemical cousins polymerized into a primitive form of RNA. Thus there was parasitic RNA inside these cells, forming a separate form of life, which was pure replication without metabolism. To quote Dyson: ‘Then the RNA invented viruses. RNA found a way to package itself in a little piece of cell membrane, and travel around freely and independently. Stage two of life has the garbage bags still unorganized and chemically random, but with RNA zooming around in little packages we call viruses carrying genetic information from one cell to another. That is my version of the RNA world. It corresponds to what Manfred Eigen considered to be the beginning of life, which I regard as stage two. You have RNA living independently, replicating, travelling around, sharing genetic information between all kinds of cells.’

Stage 3. This stage started when the protein and the RNA systems started to collaborate. This happened after the emergence of the ribosome. Although this arrangement had the rudiments of the modern cell, the genetic information was shared mostly via viruses travelling from cell to cell. This was some kind of open-source heredity. The chemical inventions made by one cell could be shared with others. Evolution went on in parallel in many different cells. The best chemical devices could be shared between different cells and combined, so the chemical evolution was very rapid, as it occurred in parallel by many pathways. This is when most of the basic biochemical inventions must have been made.

The emergence of the ribosome is still a scientific mystery. This is one reason why I did not dwell on it when I discussed in Part 9 the role of autocatalytic sets of molecules for explaining the emergence of complex molecules. The ribosome plays a crucial role in the production of proteins in the cell. This production involves the transcription of a stretch of DNA into a portable form, namely the mRNA. The mRNA travels to the cytoplasm of the cell, where the information is conveyed to the ribosome. This is where the code is read, and the corresponding amino acid is brought into the ribosome. Each amino acid comes connected to a specific tRNA molecule. There is a three-letter recognition site on the tRNA that is complementary to, and pairs with, the three-letter code sequence for that amino acid on the mRNA.

Stage 4. Speciation and sex appeared in Stage 4, and that marked the beginning of the Darwinian era, when species appeared. ‘Some cells decided it was advantageous to keep their intellectual property private, to have sex only with themselves or with the members of their own species, thereby defining species. That was then the state of life for the next two billion years, the Archeozoic and Proterozoic eras. It was a rather stagnant phase of life, continued for two billion years without evolving fast.’

Stage 5. Multicellular organisms appeared in Stage 5, which also involved death.

Stage 6. This is the stage when we humans appeared.

12.7 The RNA-World Hypothesis

At present there is a strong section of opinion, embodied in the so-called RNA-World hypothesis, according to which RNA acted both as an information-storagemolecule and as an enzyme at an early stage in theappearance of life. In other words, life started as nude replicating RNA molecules. This view of the origin of life had its genesis in the discovery, made in the mid-1980s by Thomas Cech and coworkers, that certain RNA sequences called ribozymes can themselves act as enzymes and catalyze reactions. The dual functionality of RNA might have allowed for the existence of an RNA species that could replicate itself andthus seed the beginning of molecular evolution. RNA is indeed known to be involved in a number of fundamental cell biological processes. Moreover, the ribosome is made up largely of RNA sequences, along with some proteins, and the ribosome machinery is almost identical throughout the living world; perhaps it existed almost from the beginning of life on Earth.

Several scientists have expressed reservations about this model. I shall quote the objections of one of them, namely Stuart Kauffman, author of the 1995 book At Home in the Universe: The Search for the Laws of Self-Organization and Complexity, and the 2000 book Investigations.

It is difficult to get RNA strands to reproduce in a test tube. ‘No one has succeeded in achieving experimental conditions in which a single-stranded DNA or RNA could line up free nucleotides, one by one, as complements to a single strand, catalyze the ligation of the free nucleotides into a second strand, melt the two strands apart, then enter another replication cycle. It just has not worked’ (Kauffman 2000).

Even if life did tend to originate and evolve by the RNA route, naked RNA molecules must have suffered an ‘error catastrophe’ during the replication processes, thus corrupting the genetic message from generation to generation. In present-day cells, such errors (mutations) are kept to a minimum by ‘proofreading’ and ‘editing’ enzymes.

RNA-based life, even if it did emerge, was not complex enough to sustain itself. In other words, it was too far from the edge of chaos where complexity thrives best. Why viruses do not have life? Why is it that the simplest free-living cells are the so-called pleuromona, and nothing less complex than them? Pleuromona are the simplest known bacteria, and they are complete with cell membrane, genes, RNA, protein-synthesizing machinery, proteins. All free-living cells have at least the minimal molecular diversity of pleuromona. Why nothing simpler exists that is alive on its own? The nude RNA or the nude ribozyme polymerase idea for the origin of life offers no decent explanation for the observed minimum necessary complexity of any life form.

Investigations

self-organization

climb the complexity ladder till they reach the ‘phase transition’ region (or the edge of chaos) in state space

I wish to say that life is an expected, emergent property of complex chemical reaction networks. Under rather general conditions, as the diversity of molecular species in a reaction system increases, a phase transition is crossed beyond which the formation of collectively autocatalytic sets of molecules suddenly becomes almost inevitable. If so, we are birthed by molecular diversity, children of second-generation stars.

Stuart Kauffman, Investigations (2000)

12.8 The First Step towards the Evolution of Consciousness

The emergence of self-replicators like RNA and DNA marked the first step towards the evolution of consciousness. Of course, nobody equates a self-replicator with a conscious entity. But a self-replicator which has repeatedly survived the depredations of the second law of thermodynamics, namely its own decay into a state of disorder and destruction, must have entailed the existence of a reason for surviving.

In the beginning, there were no reasons, only causes and effects. No self-interests, no purpose, no function, no teleology. The emergence of replicators changed all that. The fact that some of them have survived means (in anthropomorphic terms) that they had a kind of ‘interest’ in self-replication.

The blind forces of Nature did not distinguish between a piece of rock and a replicator. Nobody cared (there was nobody to care) whether or not a rock or a replicator survived for any length of time. But the fact is that we can see that a certain kind of replicator has indeed survived by repeated self-replication. And this has happened in spite of the fact that nobody did anything deliberately to ensure the survival of the replicator. Survival by self-replication requires the existence of a suitably conducive environment. There were all kinds of replicators (chemical entities) to start with, but those which could avoid the ‘bad’ conditions and seek ‘good’ conditions had a better chance of survival. This was just natural selection at the molecular level.

Such a successfully self-replicating entity thus ‘creates’ for itself a ‘point of view’, according to which it partitions the environment into ‘favourable’, ‘unfavourable’, and ‘neutral’. If this chemical entity is such that there is a better chance that it would ’seek’ favourable environments and ‘avoid’ unfavourable ones, it has the equivalent of what we humans recognize as ’self interest’. The chemical entity is not doing anything ‘consciously’, but the end result is the same. As Daniel Dennett (1984) pointed out, once an entity comes to have ‘interests’ and is a ‘problem-solver,’ the world and its events begin creating reasons for it. The first problem faced by such primitive problem-solvers was to ‘learn’ how to recognize and act on the reasons that their very existence brought into existence.

What is more, boundaries become important for any self-preserving entity. The entity must ‘know’ what to preserve; the boundaries limit and determine what needs to be preserved by self-replication. This primordial form of ’selfishness’ is a characteristic of life. The distinction between everything on the inside of a closed boundary and everything outside is a central feature of all biological processes.

Thus the emergence of self-replicating entities in Nature led to:

reasons to recognize;
points of view from which to recognize or evaluate; and
the need to distinguish between ‘here inside’ and ‘the external world.’

The point of view of a modern-day conscious observer is, of course, not identical to, but is a sophisticated descendant of, the primordial points of view of the first self-replicators which divided their worlds into good and bad.

12.9 Concluding Remarks

First an updated (2008) summary of Dyson’s model, and in his own words: ‘The essential idea (regarding the origin of life) is that you separate metabolism from replication. We know modern life has both metabolism and replication, but they’re carried out by separate groups of molecules. Metabolism is carried out by proteins and all kinds of other molecules, and replication is carried out by DNA and RNA. That maybe is a clue to the fact that they started out separate rather than together. So my version of the origin of life is that it started with metabolism only.’

I mentioned the RNA-world hypothesis in Section 12.6, which is at variance with what Dyson and Kauffman have been emphasizing. I am inclined to agree with Kauffman that the RNA-world hypothesis is probably not a good one because it ignores the minimum-necessary-complexity requirement for a live system to sustain and propagate itself. The tendency for the edge-of-chaos existence of complex adaptive systems (which I shall discuss in Part 14 of this series) is another argument in favour of Dyson’s model, which involves the existence of proteins before RNA emerged.

Following Dennett (1984), I have made an important point in this article that the emergence of self-replicators like RNA and DNA provided the first reason for the evolution of consciousness.

In 1944, Oswald Avery successfully converted one strain or species (the so-called R-strain) of pneumococci bacteria into another (the S-strain) by exposing the R-strain to an extract of the heat-killed S-strain (this extract was shown to consist of pure DNA). In June 2007, Craig Venter announced the results of the work done in his laboratory on genome transplantation. He reported the successful transformation of one type of bacteria into another; the new bacterium was dictated entirely by the transplanted chromosome. In other words, one species became another. We can say that he created life in the form of a new species (without any ‘divine’ intervention). This was an event of enormous significance. Life had been created in the laboratory, even though there was a concomitant annihilation of a different form of life. The next target is to create life starting from ’scratch’, i.e. by not using any precursors derived from living organisms. I have no doubt that this will happen in the near future. Such is the power of the scientific method we humans have invented and nurtured.

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Biocentrism Demystified: A Response to Deepak Chopra and Robert Lanza’s Notion of a Conscious Universe

Coauthors- — Mon, 14 Dec 2009 05:53:19 +0000

“It is almost irresistible for humans to believe that we have some special relation to the universe, that human life is not just a more-or-less farcical outcome of a chain of accidents reaching back to the first three minutes, but that we were somehow built in from the beginning.”

-Steven Weinberg

“You are here to enable the divine purpose of the universe to unfold. That is how important you are.”

-Eckhart Tolle

1. Introduction

The impulse to see human life as central to the existence of the universe is manifested in the mystical traditions of practically all cultures. It is so fundamental to the way pre-scientific people viewed reality that it may be, to a certain extent, ingrained in the way our psyche has evolved, like the need for meaning and the idea of a supernatural God. As science and reason dismantle the idea of the centrality of human life in the functioning of the objective universe, the emotional impulse has been to resort to finer and finer misinterpretations of the science involved. Mystical thinkers use these misrepresentations of science to paint over the gaps in our scientific understanding of the universe, belittling, in the process, science and its greatest heroes.

In their recent article in The Huffington Post, biologist Robert Lanza and mystic Deepak Chopra put forward their idea that the universe is itself a product of our consciousness, and not the other way around as scientists have been telling us. In essence, these authors are re-inventing idealism, an ancient philosophical concept that fell out of favour with the advent of the scientific revolution. According to the idealists, the mind creates all of reality. Many ancient Eastern and Western philosophical schools subscribe to this idealistic notion of the nature of reality. In the modern context, idealism has been supplemented with a brand of quantum mysticism and relabeled as biocentrism. According to Chopra and Lanza, this idea makes Darwin’s theory of the biological evolution and diversification of life insignificant. Both these men, although they come from different backgrounds, have independently expressed these ideas before with some popular success. In the article under discussion their different styles converge to present a uniquely mystical and bizarre worldview, which we wish to debunk here.

2. Biocentrism Misinterprets Several Scientifically Testable Truths

The scientific background to the biocentrism idea is described in Robert Lanza’s book Biocentrism: How Life and Consciousness Are the Keys to Understanding the True Nature of the Universe, in which Lanza proposes that biology and not physics is the key to understanding the universe. Vital to his proposal is the idea that the universe does not really exist unless it is being observed by a conscious observer. To support this idea, Lanza makes a series of claims:

(a) Lanza questions the conventional idea that space and time exist as objective properties of the universe. In doing this, he argues that space and time are products of human consciousness and do not exist outside of the observer. Indeed, Lanza concludes that everything we perceive is created by the act of perception.

The intent behind this argument is to help consolidate the view that subjective experience is all there is. However, if you dig into what Lanza says it becomes clear that he is positioning the relativistic nature of reality to make it seem incongruous with its objective existence. His reasoning relies on a subtle muddling of the concepts of subjectivity and objectivity. Take, for example, his argument here:

“Consider the color and brightness of everything you see ‘out there.’ On its own, light doesn’t have any color or brightness at all. The unquestionable reality is that nothing remotely resembling what you see could be present without your consciousness. Consider the weather: We step outside and see a blue sky - but the cells in our brain could easily be changed so we ’see’ red or green instead. We think it feels hot and humid, but to a tropical frog it would feel cold and dry. In any case, you get the point. This logic applies to virtually everything.“

There is only some partial truth to Lanza’s claims. Color is an experiential truth - that is, it is a descriptive phenomenon that lies outside of objective reality. No physicist will deny this. However, the physical properties of light that are responsible for color are characteristics of the natural universe. Therefore, the sensory experience of color is subjective, but the properties of light responsible for that sensory experience are objectively true. The mind does not create the natural phenomenon itself; it creates a subjective experience or a representation of the phenomenon.

Similarly, temperature perception may vary from species to species, since it is a subjective experience, but the property of matter that causes this subjective experience is objectively real; temperature is determined by the average kinetic energy of the molecules of matter, and there is nothing subjective about that. Give a thermometer to a human and to an ass: they would both record the same value for the temperature at a chosen spot of measurement.

The idea that ‘color’ is a fact of the natural universe has been described by G. E. Moore as a naturalistic fallacy. Also, the idea that color is created by an intelligent creator is a supernaturalistic fallacy. It can be said that the idea that color is created objectively in the universe by the subjective consciousness of the observer is an anthropic fallacy. The correct view is that ‘color’ is the subjective sensory perception by the observer of a certain property of the universe that the observer is a part of.

Time and space receive similar treatment as color and heat in Lanza’s biocentrism. Lanza reaches the conclusion that time does not exist outside the observer by conflating absolute time (which does not exist) with objective time (which does). In 2007 Lanza made his argument using an ancient mathematical riddle known as Zeno’s Arrow paradox. In essence, Zeno’s Arrow paradox involves motion in space-time. Lanza says:

“Even time itself is not exempted from biocentrism. Our sense of the forward motion of time is really the result of an infinite number of decisions that only seem to be a smooth continuous path. At each moment we are at the edge of a paradox known as The Arrow, first described 2,500 years ago by the philosopher Zeno of Elea. Starting logically with the premise that nothing can be in two places at once, he reasoned that an arrow is only in one place during any given instance of its flight. But if it is in only one place, it must be at rest. The arrow must then be at rest at every moment of its flight. Logically, motion is impossible. But is motion impossible? Or rather, is this analogy proof that the forward motion of time is not a feature of the external world but a projection of something within us? Time is not an absolute reality but an aspect of our consciousness.”

In a more recent article Lanza brings up the implications of special relativity on Zeno’s Arrow paradox. He writes:

“Consider a film of an archery tournament. An archer shoots an arrow and the camera follows its trajectory. Suddenly the projector stops on a single frame — you stare at the image of an arrow in mid-flight. The pause enables you to know the position of the arrow with great accuracy, but it’s going nowhere; its velocity is no longer known. This is the fuzziness described by in the uncertainty principle: sharpness in one parameter induces blurriness in the other. All of this makes perfect sense from a biocentric perspective. Everything we perceive is actively being reconstructed inside our heads. Time is simply the summation of the ‘frames’ occurring inside the mind. But change doesn’t mean there is an actual invisible matrix called “time” in which changes occur. That is just our own way of making sense of things.”

In the first case Lanza seems to state that motion is logically impossible (which is a pre-relativistic view of the paradox) and in the next case he mentions that uncertainty is present in the system (a post-relativistic model of motion). In both cases, however, Lanza’s conclusion is the same - biocentrism is true for time. No matter what the facts about the nature of time, Lanza concludes that time is not real. His model is unfalsifiable and therefore cannot be a part of science. What Lanza doesn’t let on is that Einstein’s special-relativity theory removes the possibility of absolute time, not of time itself. Zeno’s Arrow paradox is resolved by replacing the idea of absolute time with Einstein’s relativistic coupling of space and time. Space-time has an uncertainty in quantum mechanics, but it is not nonexistent. The idea of time as a series of sequential events that we perceive and put together in our heads is an experiential version of time. This is the way we have evolved to perceive time. This experiential version of time seems absolute, because we evolved to perceive it that way. However, in reality time is relative. This is a fundamental fact of modern physics. Time does exist outside of the observer, but allows us only a narrow perception of its true nature.

Space is the other property of the universe that Lanza attempts to describe as purely a product of consciousness. He says “Wave your hand through the air. If you take everything away, what’s left? The answer is nothing. So why do we pretend space is a thing”. Again, Einstein’s theory of special relativity provides us with objective predictions that we can look for, such as the bending of space-time. Such events have been observed and verified multiple times. Space is a ‘thing’ as far as the objective universe is concerned.

Lanza says “Space and time are simply the mind’s tools for putting everything together.” This is true , but there is a difference between being the ‘mind’s tools’ and being created by the mind itself. In the first instance the conscious perception of space and time is an experiential trick that the mind uses to make sense of the objective universe, and in the other space and time are actual physical manifestations of the mind. The former is tested and true while the latter is an idealistic notion that is not supported by science. The experiential conception of space and time is different from objective space and time that comprise the universe. This difference is similar to how color is different from photon frequency. The former is subjective while the latter is objective.

Can Lanza deny all the evidence that, whereas we humans emerged on the scene very recently, our Earth and the solar system and the universe at large have been there all along? What about all the objective evidence that life forms have emerged and evolved to greater and greater complexity, resulting in the emergence of humans at a certain stage in the evolutionary history of the Earth? What about all the fossil evidence for how biological and other forms of complexity have been evolving? How can humans arrogate to themselves the power to create objective reality?

Much of Lanza’s idealism arises from a distrust/incomprehension of mathematics. He writes:

“In order to account for why space and time were relative to the observer, Einstein assigned tortuous mathematical properties to an invisible, intangible entity that cannot be seen or touched. This folly continues with the advent of quantum mechanics.”

Why should the laws of Nature ‘bother’ about whether you can touch something or not? The laws of Nature have been there long before Lanza appeared on the scene. Since he cannot visualize how the mathematics describes an objective universe outside of experience, Lanza announces that reality itself does not exist unless created by the act of observation. Some cheek!

(b) Lanza claims that without an external observer, objects remain in a quantum probabilistic state. He conflates this observer with consciousness (which he admits to being “subjective experience”). Therefore, he claims, without consciousness any possible universe will only exist as probabilities. The misunderstanding of quantum theory that Lanza is promoting is addressed further in the article in the section on quantum theory (Section 4.).

(c) The central argument from Lanza is a hard version of the anthropic principle. Lanza says:

“Why, for instance, are the laws of nature exactly balanced for life to exist? There are over 200 physical parameters within the solar system and universe so exact that it strains credulity to propose that they are random — even if that is exactly what contemporary physics baldly suggests. These fundamental constants (like the strength of gravity) are not predicted by any theory — all seem to be carefully chosen, often with great precision, to allow for existence of life. Tweak any of them and you never existed. “

This reveals a total lack of understanding of what the anthropic principle really says. So let us take a good, detailed, look at this principle.

3. The Planetary Anthropic Principle

And the beauty of the anthropic principle is that it tells us, against all intuition, that a chemical model need only predict that life will arise on one planet in a billion billion to give us a good and entirely satisfying explanation for the presence of life here.

Richard Dawkins, The God Delusion (2007)

The anthropic principle was first enunciated by the mathematician Brandon Carter in 1974. Further elaboration and consolidation came in 1986 in the form of a book The Anthropic Cosmological Principle by Barrow and Tipler. There are quite a few versions of the principle doing the rounds. The scientifically acceptable version, also called the ‘weak’ (or planetary) version, states that: The particular universe in which we find ourselves possesses the characteristics necessary for our planet to exist and for life, including human life, to flourish here.

In particle physics and cosmology, we humans have had to introduce ‘best fit’ parameters (fundamental constants) to explain the universe as we see it. Slightly different values for some of the critical parameters would have led to entirely different histories of the cosmos. Why do these parameters have the values they have? According to a differently worded form of the weak version of the anthropic principle stated above: the parameters and the laws of physics can be taken as fixed; it is simply that we humans have appeared in the universe to ask such questions at a time when the conditions were just right for our life.

This version suffices to explain quite a few ‘coincidences’ related to the fact that the conditions for our evolution and existence on the planet Earth happen to be ‘just right’ for that purpose. Life as we know it exists only on planet Earth. Here is a list of favourable necessary conditions for its existence, courtesy Dawkins (2007):

Availability of liquid water is one of the preconditions for our kind of life. Around a typical star like our Sun, there is an optimum zone (popularly called the ‘Goldilocks zone’), neither so hot that water would evaporate, nor so cold that water would freeze, such that planets orbiting in that zone can sustain liquid water. Our Earth is one such planet.
This optimum orbital zone should be circular or nearly circular. Once again, our Earth fulfils that requirement. A highly elliptical orbit would take the planet sometimes too close to the Sun, and sometimes too far, during its cycle. That would result in periods when water either evaporates or freezes. Life needs liquid water all the time.
The location of the planet Jupiter in our Solar system is such that it acts like a ‘massive gravitational vacuum cleaner,’ intercepting asteroids that would have been otherwise lethal to our survival.
Planet Earth has a single relatively large Moon, which serves to stabilize its axis of rotation.
Our Sun is not a binary star. Binary stars can have planets, but their orbits can get messed up in all sorts of ways, entailing unstable or varying conditions, inimical for life to evolve and survive.

Most of the planets of stars in our universe are not in the Goldilocks zones of their parent stars. This is understandable because, as the above list of favorable conditions shows, the probability for this to happen must be very low indeed. But howsoever low this probability is, it is not zero: The proof is that life does indeed exist on Earth.

What we have listed above are just some necessary conditions. They are by no means sufficient conditions as well. With all the above conditions available on Earth, another highly improbable set of phenomena occurred, namely the actual origin of life. This origin was a set of highly improbable (but not impossible) set of chemical events, leading to the emergence of a mechanism for heredity. This mechanism came in the form of emergence of some kind of genetic molecules like RNA. This was a highly improbable thing to happen, but our existence implies that such an event, or a sequence of events, did indeed take place. Once life had originated, Darwinian evolution of complexity through natural selection (which is not a highly improbable set of events) did the rest and here we are, discussing such questions.

Like the origin of life, another extremely improbable event (or a set of events) was the emergence of the sophisticated eukaryotic cell (on which the life of we humans is based). We invoke the anthropic principle again to say that, no matter how improbable such an event was statistically, it did indeed happen; otherwise we humans would not be here. The occurrence of all such one-off highly improbable events can be explained by the anthropic principle.

Before we discuss the cosmological or ’strong’ version of the anthropic principle, it is helpful to recapitulate the basics of quantum theory.

4. Quantum Theory

In conventional quantum mechanics we use wave functions, ψ, to represent quantum states. The wave function plays a role somewhat similar to that of trajectories in classical mechanics. The Schrödinger equation describes how the wave function of a quantum system evolves with time. This equation predicts a smooth and deterministic time-evolution of the wave function, with no discontinuities or randomness. Just as trajectories in classical mechanics describe the evolution of a system in phase space from one time step to the next, the Schrödinger equation transforms the wave function at time t₀ (corresponding to a specific point in phase space) to its value ψ(t) at another time t. The physical interpretation of the wave function is that |ψ|²is the probability of occurrence of the state of the system at a given point in phase space.

An elementary particle can exist as a superposition of two or more alternative quantum states. Suppose its energy can take two values, E₁ and E₂. Let u₁ and u₂denote the corresponding wave functions. The quantum interpretation is that the system exists in both the states, with u₁²and u₂² as the respective probabilities. Thus we move from a pure state to a mixture or ensemble of states. What is more, something striking happens when we humans observe such a system, say an electron, with an instrument. At the moment of observation, the wave function appears to collapse into only one of the possible alternative states, the superposition of which was described by the wave function before the event of measurement. That is, a quantum state becomes decoherent when measured or monitored by the environment. This amounts to the introduction of a discontinuity in the smooth evolution of the wave function with time.

This apparent collapse of the wave function does not follow from the mathematics of the Schrödinger equation, and was, in the early stages of the history of quantum mechanics, introduced ‘by hand’ as an additional postulate. That is, one chose to introduce the interpretation that there is a collapse of the wave function to the state actually detected by the measurement in the ‘real’ world, to the exclusion of other states represented in the original wave function. This (unsatisfactory) dualistic interpretation of quantum mechanics for dealing with the measurement problem was suggested by Bohr and Heisenberg at a conference in Copenhagen in 1927, and is known as the Copenhagen interpretation.

Another basic notion in standard quantum mechanics is that of time asymmetry. In classical mechanics we make the reasonable-looking assumption that, once we have formulated the Newtonian (or equivalent) equations of motion for a system, the future states are determined by the initial conditions. In fact, we can not only calculate the future conditions from the initial conditions, we can even calculate the initial conditions if the future conditions or states are known. This is time symmetry. In quantum mechanics, the uncertainty principle destroys the time symmetry. There can be now a one-to-many relationship between initial and final conditions. Two identical particles, in identical initial conditions, need not be observed to be in the same final conditions at a later time.

Multiple universes

Hugh Everett, during the mid-1950s, expressed total dissatisfaction with the Copenhagen interpretation: ‘The Copenhagen Interpretation is hopelessly incomplete because of its a priori reliance on classical physics … as well as a philosophic monstrosity with a “reality” concept for the macroscopic world and denial of the same for the microcosm.’ The Copenhagen interpretation implied that equations of quantum mechanics apply only to the microscopic world, and cease to be relevant in the macroscopic or ‘real’ world.

Everett offered a new interpretation, which presaged the modern ideas of quantum decoherence. Everett’s ‘many worlds’ interpretation of quantum mechanics is now taken more seriously, although not entirely in its original form. He simply let the mathematics of the quantum theory show the way for understanding logically the interface between the microscopic world and the macroscopic world. He made the observer an integral part of the system being observed, and introduced a universal wave function that applies comprehensively to the totality of the system being observed and the observer. This means that even macroscopic objects exist as quantum superpositions of all allowed quantum states. There is thus no need for the discontinuity of a wave-function collapse when a measurement is made on the microscopic quantum system in a macroscopic world.

Wave function bifurcation

Everett examined the question: What would things be like if no contributing quantum states to a superposition of states are banished artificially after seeing the results of an observation? He proved that the wave function of the observer would then bifurcate at each interaction of the observer with the system being observed. Suppose an electron can have two possible quantum states A and B, and its wave function is a linear superposition of these two. The evolution of the composite or universal wave function describing the electron and the observer would then contain two branches corresponding to each of the states A and B. Each branch has a copy of the observer, one which sees state A as a result of the measurement, and the other which sees state B. In accordance with the all-important principle of linear superposition in quantum mechanics, the branches do not influence each other, and each embarks on a different future (or a different ‘universe’), independent of the other. The copy of the observer in each universe is oblivious to the existence of other copies of itself and other universes, although the ‘full reality’ is that each possibility has actually happened. This reasoning can be made more abstract and general by removing the distinction between the observer and the observed, and stating that, at each interaction among the components of the composite system, the total or universal wave function would bifurcate as described above, giving rise to multiple universes or many worlds.

A modern and somewhat different version of this interpretation of quantum mechanics introduces the term quantum decoherence to rationalise how the branches become independent, and how each turns out to represent our classical or macroscopic reality. Quantum computing is now a reality, and it is based on such understanding of quantum mechanics.

Parallel histories

Richard Feynman formulated a different version of the many-worlds idea, and spoke in terms of multiple or parallel histories of the universe (rather than multiple worlds or universes). This work, done after World War II, fetched him the Nobel Prize in 1965. Feynman, whose path integrals are well known in quantum mechanics, suggested that, when a particle goes from a point P to a point Q in phase space, it does not have just a single unique trajectory or history. [It should be noted that, although we normally associate the word 'history' only with past events, history in the present context can refer to both the past and the future. A history is merely a narrative of a time sequence of event - past, present, or future.] Feynman proposed that every possible path or trajectory from P to Q in space-time is a candidate history, with an associated probability. The wave function for every such trajectory has an amplitude and a phase. The path integral for going from P to Q is obtained as the weighted vector sum, or integration over all such individual paths or histories. Feynman’s rules for assigning the amplitudes and phases for computing the sum over histories happen to be such that the effects of all except the one actually measured for a macroscopic object get cancelled out. For sub-microscopic particles, of course, the cancellation is far from complete, and there are indeed competing histories or parallel universes.

Quantum Darwinism

A different resolution to the problem of interfacing the microscopic quantum description of reality with macroscopic classical reality is offered by what has been called ‘quantum Darwinism.’ This formalism does not require the existence of an observer as a witness of what occurs in the universe. Instead, the environment is the witness. A selective witness at that, rather like natural selection in Darwin’s theory of evolution. The environment determines which quantum properties are the fittest to survive (and be observed, for example, by humans). Many copies of the fitter quantum property get created in the entire environment (’redundancy’). When humans make a measurement, there is a much greater chance that they would all observe and measure the fittest solution of the Schrödinger equation, to the exclusion (or near exclusion) of other possible outcomes of the measurement experiment.

In a computer experiment, Blume-Kohout and Zurek (2007) demonstrated quantum Darwinism (http://www.arxiv.org/abs/0704.3615) in zero-temperature quantum Brownian motion (QBM). A harmonic oscillator system (S) is made to evolve in contact with a bath (ε) of harmonic oscillators. The question asked is: How much information about S can an observer extract from the bath ε? ε consists of subenvironments ε_i; i = 1, 2, 3, … Each observer has exclusive access to a fragment F consisting of m subenvironments. The so-called ‘mutual information entropy’ is calculated from the quantum mutual information between S and F.

An important result of this approach is that substantial redundancy appears in the QBM model; i.e., multiple redundant records get made in the environment. As the authors state, this redundancy accounts for the objectivity and the classicality; the environment is a witness, holding many copies of the evidence. When humans make a measurement, it is most likely that they would all interact with one of the stable recorded copies, rather than directly with the actual quantum system, and thus observe and measure the classical value, to the exclusion of other possible outcomes of the measurement experiments.

Gell-Mann’s coarse-graining interpretation of quantum mechanics

For this interpretation, let us first understand the difference between fine-grained and coarse-grained histories of the universe. Completely

Murray Gel-Mann

fine-grained histories of the universe are histories that give as complete a description as possible of the entire universe at every moment of time. Consider a simplified universe in which elementary particles have no attributes other than positions and momenta, and in which the indistinguishability among particles of a given type is ignored. Then, one kind of fine-grained history of the simplified universe would be one in which the positions of all the particles are known at all times. Unlike classical mechanics which is deterministic, quantum mechanics is probabilistic. One might think that we can write down the probability for each possible fine-grained history. But this is not so. It turns out that the ‘interference’ terms between fine-grained histories do not usually cancel out, and we cannot assign probabilities to the fine-grained histories. One has to resort to coarse-graining to be able to assign probabilities to the histories. Murray Gell-Mann and coworkers applied this approach to a description of the quantum-mechanical histories of the universe. It was shown that the interference terms get cancelled out on coarse-graining. Thus we can work directly with wave functions, rather than having to work with wave-function amplitudes, and then there is no problem interfacing the microscopic description with the macroscopic world of measurements etc.

Gell-Mann also emphasized the point that the term ‘many worlds or universes’ should be substituted by ‘many alternative histories of the universe’, with the further proviso that the many histories are not ‘equally real’; rather they have different probabilities of occurrence.

5. The Cosmological Anthropic Principle

Some quantum cosmologists like to talk about a so-called anthropic principle that requires conditions in the universe to be compatible with the existence of human beings. A weak form of the principle states merely that the particular branch history on which we find ourselves possesses the characteristics necessary for our planet to exist and for life, including human life, to flourish here. In that form, the anthropic principle is obvious. In its strongest form, however, such a principle will supposedly apply to the dynamics of the elementary particles and the initial conditions of the universe, somehow shaping those fundamental laws so as to produce human beings. That idea seems to me so ridiculous as to merit no further discussion.

Murray Gell-Mann, The Quark and the Jaguar

Much confusion and uncalled-for debate has been engendered by the (scientifically unsound) ’strong’ or cosmological version of the anthropic principle, which is sometimes stated as follows: Since the universe is compatible with the existence of human beings, the dynamics of the elementary particles and the initial conditions of the universe must have been such that they shaped the fundamental laws so as to produce human beings. This is clearly untenable. There are no grounds for the existence of a ‘principle’ like this. A scientifically untenable principle is no principle at all. No wonder, the Nobel laureate Gell-Mann, as quoted above, described it as ‘so ridiculous as to merit no further discussion.’

The chemical elements needed for life were forged in stars, and then flung far into space through supernova explosions. This required a certain amount of time. Therefore the universe cannot be younger than the lifetime of stars. The universe cannot be too old either, because then all the stars would be ‘dead’. Thus, life can exist only when the universe has just the age that we humans measure it to be, and has just the physical constants that we measure them to be.

It has been calculated that if the laws and fundamental constants of our universe had been even slightly different from what they are, life as we know it would not have been possible. Rees (1999), in the book Just Six Numbers, listed six fundamental constants which together determine the universe as we see it. Their fine-tuned mutual values are such that even a slightly different set of these six numbers would have been inimical to our emergence and existence. Consideration of just one of these constants, namely the strength of the strong interaction (which determines the binding energies of nuclei), is enough to make the point. It is defined as that fraction of the mass of an atom of hydrogen which is released as energy when hydrogen atoms fuse to form an atom of helium. Its value is 0.007, which is just right (give or take a small acceptable range) for any known chemistry to exist, and no chemistry means no life. Our chemistry is based on reactions among the 90-odd elements. Hydrogen is the simplest among them, and the first to occur in the periodic table. All the other elements in our universe got synthesised by fusion of hydrogen atoms. This nuclear fusion depends on the strength of the strong or nuclear interaction, and also on the ability of a system to overcome the intense Coulomb repulsion between the fusing nuclei. The creation of intense temperatures is one way of overcoming the Coulomb repulsion. A small star like our Sun has a temperature high enough for the production of only helium from hydrogen. The other elements in the periodic table must have been made in the much hotter interiors of stars larger than our Sun. These big stars may explode as supernovas, sending their contents as stellar dust clouds, which eventually condense, creating new stars and planets, including our own Earth. That is how our Earth came to have the 90-odd elements so crucial to the chemistry of our life. The value 0.007 for the strong interaction determined the upper limit on the mass number of the elements we have here on Earth and elsewhere in our universe. A value of, say, 0.006, would mean that the universe would contain nothing but hydrogen, making impossible any chemistry whatsoever. And if it were too large, say 0.008, all the hydrogen would have disappeared by fusing into heavier elements. No hydrogen would mean no life as we know it; in particular there would be no water without hydrogen.

Similarly for the other finely-tuned fundamental constants of our universe. Existence of humans has become possible because the values of the fundamental constants are what they are; had they been different, we would not exist; that is how the anthropic principle (planetary or cosmological, weak or strong) should be stated. The weak version is the only valid version of the principle.

But why does the universe have these values for the fundamental constants, and not some other set of values? Different physicists and cosmologists have tried to answer this question in different ways, and the investigations go on. One possibility is that there are multiple universes, and we are in one just right for our existence. Another idea is based on string theory.

6. String Theory and the Anthropic Principle

A ’string’ is a fundamental 1-dimensional object, postulated to replace the concept of structureless elementary particles. Different vibrational modes of a string give rise to the various elementary particles (including the graviton). String theory aims to unite quantum mechanics and the general theory of relativity, and is thus expected to be a unified ‘theory of everything.’ When this theory makes sufficient headway, the six fundamental constants identified by Rees will turn out to be inter-related, and not free to have any arbitrary values. But this still begs the question asked above: Why this particular set of fundamental constants, and not another? Hawking (1988) asked an even deeper question: ‘Even if there is only one possible unified theory, it is just a set of rules and equations. What is it that breathes fire into the equations and makes a universe for them to describe? The usual approach of science of constructing a mathematical model cannot answer the questions of why there should be a universe for the model to describe. Why does the universe go to all the bother of existing?’

Our universe is believed to have started at the big bang, shown by Hawking and Penrose in the 1970s to be a singularity point is space-time (some physicists disagree with the singularity idea). The evidence for this seems to be that the universe has been expanding (’inflating’) ever since then. It so happens that we have no knowledge of the set of initial boundary conditions at the moment of the big bang. Moreover, as Hawking and Hertog said in 2006, things could be a little simpler ‘if one knew that the universe was set going in a particular way in either the finite or infinite past.’ Therefore Hawking and coworkers argued that it is not possible to adopt the bottom up approach to cosmology wherein one starts at the beginning of time, applies the laws of physics, calculates how the universe would evolve with time, and then just hopes that it would turn out to be something like the universe we live in. Consequently a top down approach has been advocated by them (remember, this is just a model), wherein we start with the present and work our way backwards into the past. According to Hawking and Hertog (2006), there are many possible histories (corresponding to successive unpredictable bifurcations in phase space), and the universe has lived them all. Not only that, there is also an anthropic angle to this scenario:

As mentioned above, Stephen Hawking and Roger Penrose had proved that the moment of the big bang was a singularity, i.e. a point where gravity must have been so strong as to curve space and time in an unimaginably strong way. Under such extreme conditions our present formulation of general relativity would be inadequate. A proper quantum theory of gravity is still an elusive proposition. But, as suggested by Hawking and Hertog in 2006, because of the small size of the universe at and just after the big bang, quantum effects must have been very important. The origin of the universe must have been a quantum event. This statement has several weird-looking consequences. The basic idea is to incorporate the consequences of Heisenberg’s uncertainty principle when considering the evolution of the (very small) early universe, and combine it with Feynman’s sum-over-histories approach. This means that, starting from configuration A, the early universe could go not only to B, but also to other configurations B’, B”, etc. (as permitted by the quantum-mechanical uncertainty principle), and one has to do a sum-over-histories for each of the possibilities AB, AB’, AB”, … And each such branch corresponds to a different evolution of the universe (with different cosmological and other fundamental constants), only one or a few of them corresponding to a universe in which we humans could evolve and survive. This provides a satisfactory answer to the question: ‘why does the universe have these values for the fundamental constants, and not some other set of values?’.

The statement ‘humans exist in a universe in which their existence is possible’ is practically a tautology. How can humans exist in a universe which has values of fundamental constants which are not compatible with their existence?! Stop joking, Dr. Lanza.

The other possible universes (or histories) also exist, each with a specific probability. Our observations of the world are determining the history that we see. The fact that we are there and making observations assigns to ourselves a particular history.

Let A denote the beginning of time (if there is any), and B denote now. The state of the universe at point B can be broadly specified by recognizing the important aspects of the world around us: There are three large dimensions in space, the geometry of space is almost flat, the universe is expanding, etc. The problem is that we have no way of specifying point A. So how do we perform the various sums over histories? An interesting point of the quantum mechanical sums-over-histories theory is that the answers come out right when we work with imaginary (or complex) time, rather than real time. The work of Hawking and Hertog (2006) has shown that the imaginary-time approach is crucial for understanding the origin of the universe. When the histories of the universe are added up in imaginary time, time gets transformed into space. It follows from this work that when the universe was very small, it had four spatial dimensions, and none for time. In terms of the history of the universe, it means that there is no point A, and that the universe has no definable starting point or initial boundary conditions. In this no-boundary scheme of things, we can only start from point B and work our way backwards (the top-down approach).

This approach also solves the fine-tuning problem of cosmology. Why has the universe a particular inflation history? Why does the cosmological constant (which determines the rate of inflation) have the value it has? Why did the early universe have a particular ‘fine-tuned’ initial configuration and a specific (fast) initial rate of inflation? In the no-boundary scenario there is no need to define an initial state. And there is no need for any fine tuning. What is more, the very fact of inflation, as against no inflation, follows from the theory as the most probable scenario.

Artistic Rendition of the Multiverse. Source: Nature

String theory defines a near-infinity of multiple universes. This goes well with the anthropic-principle idea that, out of the multiple choices for the fundamental constants (including the cosmological constant) for each such universe, we live in the universe that makes our existence possible. In the language of string theory, there are multiple ‘pocket’ universes that branch off from one another, each branch having a different set of fundamental constants. Naturally, we are living in one with just the right fundamental constants for our existence.

While many physicists feel uncomfortable with this unconfirmed world view, Hawking and Hertog (2006) have pointed out that the picture of a never-ending proliferation of pocket universes is meaningful only from the point of view of an observer outside a universe, and that situation (observer outside a universe) is impossible. This means that parallel pocket universes can have no effect on an actual observer inside a particular pocket.

Hawking’s work has several other implications as well. For example, in his scheme of things the string theory ‘landscape’ is populated by the set of all possible histories. All possible versions of a universe exist in a state of quantum superposition. When we humans choose to make a measurement, a subset of histories that share the specific property measured gets selected. Our version of the history of the universe is determined by that subset of histories. No wonder the cosmological anthropic principle holds. How can any rational person use the anthropic principle to justify biocentrism?

Hawking and Hertog’s theory can be tested by experiment, although that is not going to be easy. Its invocation of Heisenberg’s uncertainty principle during the early moments of the universe, and the consequent quantum fluctuations, leads to a prediction of specific fluctuations in the cosmic microwave background, and in the early spectrum of gravitational waves. These predicted fluctuations arise because there is an uncertainty in the exact shape of the early universe, which is influenced, among other things, by other histories with similar geometries. Unprecedented precision will be required for testing these predictions. In any case, gravitation waves have not even been detected yet.

In any case, good scientists are having a serious debate about the correct interpretation of the data available about life and the universe. While this goes on, non-scientists and charlatans cannot be permitted to twist facts to satisfy the hunger of humans for the feel-good or feel-important factor. The scientific method is such that scientists feel good when they are doing good science.

7. Wolfram’s Universe

Stephen Wolfram has emphasized the role of computational irreducibility when it comes to trying to understand our universe. The notion of probability (as opposed to certainty) is inherent in our worldview if quantum theory is a valid theory. Wolfram argues that this may not be a correct worldview. He does not rule out the possibility that there really is just a single, definite, rule for our universe which, in a sense, deterministically specifies how everything in our universe happens. Things only look probabilistic because of the high degree of complexity involved, particularly regarding the very structure and connectivity of space and time. It is computational irreducibility that sometimes makes certain things look incomprehensible or probabilistic, rather than deterministic. Since we are restricted to doing the computational work within the universe, we cannot expect to ‘outrun’ the universe, and derive knowledge any faster than just by watching what the universe actually does.

Wolfram points out that there is relief from this tyranny of computational irreducibility only in the patches or islands of computational reducibility. It is in those patches that essentially all of our current physics lies. In natural science we usually have to be content with making models that are approximations. Of course, we have to try to make sure that we have managed to capture all the features that are essential for some particular purpose. But when it comes to finding an ultimate model for the universe, we must find a precise and exact representation of the universe, with no approximations. This would amount to reducing all physics to mathematics. But even if we could do that and know the ultimate rule, we are still going to be confronted with the problem of computational irreducibility. So, at some level, to know what will happen, we just have to watch and see history unfold.

8. The Nature of Consciousness

One criticism of biocentrism comes from the philosopher Daniel Dennett, who says “It looks like an opposite of a theory, because he doesn’t explain how consciousness happens at all. He’s stopping where the fun begins.”

The logic behind this criticism is obvious. Without a descriptive explanation for consciousness and how it ‘creates’ the universe, biocentrism is not useful. In essence, Lanza calls for the abandonment of modern theoretical physics and its replacement with a magical solution. Here are a few questions that one might ask of the idea:

What is this consciousness?
Why does this consciousness exist?
What is the nature of the interaction between this consciousness and the universe?
Is the problem of infinite regression applicable to consciousness itself?
Even if Lanza’s interpretation of the anthropic principle is a valid argument against modern theoretical physics, does the biocentric model of consciousness create a bigger ontological problem than the one it attempts to solve?

Consider this statement by Lanza:

“Consciousness cannot exist without a living, biological creature to embody its perceptive powers of creation.“

How can consciousness create the universe if it doesn’t exist? How can the “living, biological creature” exist if the universe has not been created yet? It becomes apparent that Lanza is muddling the meaning of the word ‘consciousness.’ In one sense he equates it to subjective experience that is tied to a physical brain. In another, he assigns to consciousness a spatio-temporal logic that exists outside of physical manifestation. In this case, the above questions become: 1. What is this spatio-temporal logic?; 2. Why does this spatio-temporal logic exist? and so on…

Daniel Dennett’s criticism of biocentrism centres on Lanza’s non-explanation of the nature of consciousness. In fact, even from a biological

The Cartesian Theater

perspective Lanza’s conception of consciousness is unclear. For example, he consistently equates consciousness with subjective experience while stressing its independence from the objective universe (see Lanza’s quote below). This is an appeal to the widespread but erroneous intuition towards Cartesian Dualism. In this view, consciousness (subjective experience) belongs to a different plane of reality than the one on which the material universe is constructed. Lanza requires this general definition of consciousness to construct his theory of biocentrism. He uses it in the same way that Descartes used it - as a semantic tool to deconstruct reality. In fact, Lanza’s theory of biocentrism is a sophisticated non-explanation for the ‘brain in a vat’ problem that plagued philosophers for centuries. However, instead of subscribing to Cartesian Dualism, he attempts a Cartesian Monism by invoking quantum mechanics. To be exact, his view is Monistic Idealism - the idea that consciousness is everything- but the Cartesian bias is an essential element in his arguments.

In a dualistic or idealistic context, Lanza’s definition of consciousness as subjective experience may be acceptable. However, Lanza’s definition is incomplete from a scientific perspective. The truth is that there are difficulties in analysing consciousness empirically. In scientific terms, consciousness is a ‘hard problem’, meaning that its complete subjective nature places it beyond direct objective study. Lanza exploits this difficulty to deny science any understanding of consciousness.

Lanza trivializes the current debate in the scientific community about the nature of consciousness when he says:

“Neuroscientists have developed theories that might help to explain how separate pieces of information are integrated in the brain and thus succeed in elucidating how different attributes of a single perceived object-such as the shape, colour, and smell of a flower-are merged into a coherent whole. These theories reflect some of the important work that is occurring in the fields of neuroscience and psychology, but they are theories of structure and function. They tell us nothing about how the performance of these functions is accompanied by a conscious experience; and yet the difficulty in understanding consciousness lies precisely here, in this gap in our understanding of how a subjective experience emerges from a physical process.”

This criticism of the lack of a scientific consensus on the nature of consciousness is empty, considering that Lanza himself proposes no actual mechanism for consciousness, but still places it at the centre of his theory of the universe.

There is no need to view consciousness as such a mystery. There are some contemporary models of consciousness that are quite explanatory, presenting promising avenues for studying how the brain works. Daniel Dennett’s Multiple Drafts Model is one. According to Dennett, there is nothing mystical about consciousness. It is an illusion created by tricks in the brain. The biological machinery behind the tricks that create the illusion of consciousness is the product of successive evolutionary processes, beginning with the development of primitive physiological reactions to external stimuli. In the context of modern humans, consciousness consists of a highly dynamic process of information exchange in the brain. Multiple sets of sensory information, memories and emotional cues are competing with each other at all times in the brain, but at any one instant only one set of these factors dominates the brain. At the next instant, another set of slightly different factors are dominant. At all instants, multiple sets of information are competing with each other for dominance. This creates the illusion of a continuous stream of thoughts and experiences, leading to the intuition that consciousness comprises the entirety of the voluntary mental function of the individual. There are other materialist models, such as Marvin Minsky’s view of the brain as an emotional machine, that provide us with ways of approaching the problem from a scientific perspective without resorting to mysticism.

Consciousness is not something that requires a restructuring of objective reality. It is a subjective illusion on one level, and the mechanistic outcome of evolutionary processes on another.

“A human being is a part of a whole, called by us ‘universe’, a part limited in time and space. He experiences himself, his thoughts and feelings as something separated from the rest… a kind of optical delusion of his consciousness.”

Albert Einstein

9. Deepak Chopra Finds an Ally for Hijacking and Distorting Scientific Truths

Deepak Chopra, Lanza’s coauthor in the article, is known for making bold claims about the nature of the universe. He peddles a form of new-age Hinduism. Chopra’s ideas about a conscious universe are derived from an interpretation of Vedic teachings. He supplements this new-age Hinduism with ideas from a minority view among physicists that the Copenhagen Interpretation implies a conscious universe. This view is expounded by Amit Goswami in his book The Self-Aware Universe. In turn, Goswami and his peers were influenced by Fritjof Capra’s book The Tao of Physics in which the author attempts to reconcile reductionist science with Eastern mystical philosophies. Much of modern quantum mysticism in the popular culture can be traced back to Capra. Chopra’s philosophy is essentially a distillation of Capra’s work combined with a popular marketing strategy to sell all kinds of pseudoscientific garbage.

Considering Chopra’s reputation in the scientific community for making absurd quack claims about every subject under the sun, one must wonder about the strange pairing between the two writers. With Lanza’s experience in biomedical research, he could not possibly be in agreement with Chopra’s brand of holistic healing and quantum mysticism. Rather, it seems likely that this is an arrangement of convenience. If you look at what drives the two men, a mutually reinforced disenchantment with Darwin’s ideas emerges as a strong motive behind the pairing. Both Chopra and Lanza are disillusioned with a certain perceived implication of Darwinian evolution on human existence - that the meaning of life is inconsequential to the universe. Evolutionary biology upholds the materialist view of modern science that consciousness is a product of purely inanimate matter assembling in highly complex states. Such a view is disillusioning to anyone who craves a more central role for the human ego in determining one’s reality. The view that human life is central to existence is found in most philosophical and religious traditions. This view is so fundamental to our nature that we can say it is an intuitive reaction to the very condition of being conscious. It has traditionally been the powerful driving force behind philosophers, poets, priests, mystics and scholars of history. Darwin dismantled the idea in one clean stroke. Therefore, Darwin became the enemy. The entire theory of biocentrism is an attempt to ingrain the idea of human destiny into popular science.

The title of Chopra and Lanza’s article is “Evolution Reigns, but Darwin Outmoded”. This may mislead you to think that the article is about new discoveries in biological evolution. On reading the article, however, it becomes apparent that the authors are not talking about biological evolution at all. It is relevant to note that not once in their article do they say how Darwin has been outmoded.

Towards the end of their article, Chopra and Lanza say:

“Darwin’s theory of evolution is an enormous over-simplification. It’s helpful if you want to connect the dots and understand the interrelatedness of life on the planet — and it’s simple enough to teach to children between recess and lunch. But it fails to capture the driving force and what’s really going on.”

There is irony in dismissing the most brilliant and explanatory scientific theory in all of biology as an ‘over-simplification’, by over-simplifying it as a way to “connect the dots and understand the interrelatedness of life on the planet”. Contrast this with what Richard Dawkins said: “In 1859, Charles Darwin announced one of the greatest ideas ever to occur to a human mind: cumulative evolution by natural selection.” The irony of Chopra and Lanza’s statement is compounded by the fact that biocentrism does not address biological evolution at all! The authors are simply interested in belittling the uncomfortable implications of evolutionary theory, while not actually saying anything about the theory itself! We can safely assume that Lanza and Chopra are more concerned with the implications of Darwinian evolution on the nature of the human ego, and not on the theory of evolution by natural selection.

Interestingly, Chopra has demonstrated his dislike and ignorance of biological evolution multiple times. Here are some prize quotations from the woo-master himself (skip these if you feel an aneurysm coming):

“To say the DNA happened randomly is like saying that a hurricane could blow through a junk yard and produce a jet plane. “

“How does nature take creative leaps? In the fossil record there are repeated gaps that no “missing link” can fill. The most glaring is the leap by which inorganic molecules turned into DNA. For billions of years after the Big Bang, no other molecule replicated itself. No other molecule was remotely as complicated. No other molecule has the capacity to string billions of pieces of information that remain self-sustaining despite countless transformations into all the life forms that DNA has produced. “

“If mutations are random, why does the fossil record demonstrate so many positive mutations–those that lead to new species–and so few negative ones? Random chance should produce useless mutations thousands of times more often than positive ones. “

“Evolutionary biology is stuck with regard to simultaneous mutations. One kind of primordial skin cell, for example, mutated into scales, fur, and feathers. These are hugely different adaptations, and each is tremendously complex. How could one kind of cell take three different routs purely at random? “

“If design doesn’t imply intelligence, why are we so intelligent? The human body is composed of cells that evolved from one-celled blue-green algae, yet that algae is still around. Why did DNA pursue the path of greater and greater intelligence when it could have perfectly survived in one-celled plants and animals, as in fact it did? “

“Why do forms replicate themselves without apparent need? The helix or spiral shape found in the shell of the chambered nautilus, the centre of sunflowers, spiral galaxies, and DNA itself seems to be such a replication. It is mathematically elegant and appears to be a design that was suited for hundreds of totally unrelated functions in nature. “

“What happens when simple molecules come into contact with life? Oxygen is a simple molecule in the atmosphere, but once it enters our lungs, it becomes part of the cellular machinery, and far from wandering about randomly, it precisely joins itself with other simple molecules, and together they perform cellular tasks, such as protein-building, whose precision is millions of times greater than anything else seen in nature. If the oxygen doesn’t change physically–and it doesn’t–what invisible change causes it to acquire intelligence the instant it contacts life? “

“How can whole systems appear all at once? The leap from reptile to bird is proven by the fossil record. Yet this apparent step in evolution has many simultaneous parts. It would seem that Nature, to our embarrassment, simply struck upon a good idea, not a simple mutation. If you look at how a bird is constructed, with hollow bones, toes elongated into wing bones, feet adapted to clutching branches instead of running, etc., none of the mutations by themselves give an advantage to survival, but taken altogether, they are a brilliant creative leap. Nature takes such leaps all the time, and our attempt to reduce them to bits of a jigsaw puzzle that just happened to fall into place to form a beautifully designed picture seems faulty on the face of it. Why do we insist that we are allowed to have brilliant ideas while Nature isn’t? “

“Darwin’s iron law was that evolution is linked to survival, but it was long ago pointed out that “survival of the fittest” is a tautology. Some mutations survive, and therefore we call them fittest. Yet there is no obvious reason why the dodo, kiwi, and other flightless birds are more fit; they just survived for a while. DNA itself isn’t fit at all; unlike a molecule of iron or hydrogen, DNA will blow away into dust if left outside on a sunny day or if attacked by pathogens, x-rays, solar radiation, and mutations like cancer. The key to survival is more than fighting to see which organism is fittest. “

“Competition itself is suspect, for we see just as many examples in Nature of cooperation. Bees cooperate, obviously, to the point that when a honey bee stings an enemy, it acts to save the whole hive. At the moment of stinging, a honeybee dies. In what way is this a survival mechanism, given that the bee doesn’t survive at all? For that matter, since a mutation can only survive by breeding–”survival” is basically a simplified term for passing along gene mutations from one generation to the next-how did bees develop drones in the hive, that is, bees who cannot and never do have sex? “

“How did symbiotic cooperation develop? Certain flowers, for example, require exactly one kind of insect to pollinate them. A flower might have a very deep calyx, or throat, for example than only an insect with a tremendously long tongue can reach. Both these adaptations are very complex, and they serve no outside use. Nature was getting along very well without this symbiosis, as evident in the thousands of flowers and insects that persist without it. So how did numerous generations pass this symbiosis along if it is so specialized? “

“Finally, why are life forms beautiful? Beauty is everywhere in Nature, yet it serves no obvious purpose. Once a bird of paradise has evolved its incredibly gorgeous plumage, we can say that it is useful to attract mates. But doesn’t it also attract predators, for we simultaneously say that camouflaged creatures like the chameleon survive by not being conspicuous. In other words, exact opposites are rationalized by the same logic. This is no logic at all. Non-beautiful creatures have survived for millions of years, so have gorgeous ones. The notion that this is random seems weak on the face of it. “

Now comes the kicker. All these quotes that demonstrate a complete lack of understanding of biology, let alone the theory of evolution by natural selection, are from one single article as compiled by P. Z. Myers in his blog post in 2005. Since then, Chopra has continued to spout his ignorance of evolution over and over.

Chopra’s brand of mysticism gets its claimed legitimacy from science and its virulence from discrediting science’s core principles. He continues this practice through his association with Robert Lanza. Both Chopra and Lanza seem to be disillusioned by the perceived emptiness of a non-directional evolutionary reality. Chopra has invested much time and effort in promoting the idea that consciousness in a property of the universe itself. He finds in Lanza a keen mind with an inclination towards a similar dislike for a perceived lack of anthropocentric meaning in the nature of biological life as described by Darwin’s theory of evolution by natural selection.

10. Conclusions

Let us recapitulate the main points:

(a) Space and time exist, even though they are relative and not absolute.

(b) Modern quantum theory, long after the now-discredited Copenhagen interpretation, is consistent with the idea of an objective universe that exists without a conscious observer.

(c) Lanza and Chopra misunderstand and misuse the anthropic principle.

(d) The biocentrism approach does not provide any new information about the nature of consciousness, and relies on ignoring recent advances in understanding consciousness from a scientific perspective.

(e) Both authors show thinly-veiled disdain for Darwin, while not actually addressing his science in the article. Chopra has demonstrated his utter ignorance of evolution multiple times.

Modern physics is a vast and multi-layered web that stretches over the entire deck of cards. All other natural sciences - all truths that exist in the material world- are interrelated, held together by the mathematical reality of physics. Fundamental theories in physics are supported by multiple lines of evidence from many different scientific disciplines, developed and tested over decades. Clearly, those who propose new theories that purport to redefine fundamental assumptions or paradigms in physics have their work cut out for them. Our contention is that the theory of biocentrism, if analysed properly, does not hold up to scrutiny. It is not the paradigm change that it claims to be. It is also our view that one can find much meaning, beauty and purpose in a naturalistic view of the universe, without having to resort to mystical notions of reality.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

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COMPLEXITY EXPLAINED: 11. Cellular Automata

Vinod K. Wadhawan — Fri, 11 Dec 2009 01:57:02 +0000

(Note: All previous parts of Dr. Wadhawan’s series ‘Complexity Explained’ can be accessed through the Related Posts list at the bottom of this article.)

There is a distinction between replication and reproduction. Probably, the earliest living entities were able to reproduce but not to replicate. Cells can reproduce, but only molecules can replicate. Reproduction in the case of such primitive cells means to divide into two cells with the daughter cells inheriting approximately equal shares of the constituents of the cell. By contrast, replication for a molecule means the creation of an exact copy of itself by suitable chemical processes. Here I describe John von Neumann’s computer-simulation studies on self-reproduction, after introducing the notion of cellular automata. Studies on cellular automata help us understand life processes.

11.1 Introduction

Present-day life processes involve metabolic reproduction and replication. Freeman Dyson (1985) argued that metabolic reproduction and replication are logically separable propositions. He pointed out that Darwinian natural selection does not require replication, at least for simple creatures. According to Dyson, it is likely that life originated twice, with two separate kinds of organisms, one capable of metabolism without exact replication, and the other capable of replication without metabolism. At some stage the two features came together. He suggested, probably, that the earliest living creatures were able to reproduce but not to replicate. I shall discuss Dyson’s dual-origin-hypothesis for life in the next article in this series. Here I focus on some computer-simulation aspects of replication and reproduction.

An automaton has two components, which are now known by the names hardware and software. Roughly speaking, software embodies information, and hardware processes information. And the rough analogy to biology is: nucleic acid is software, and protein is hardware. Usually, protein is the essential component for metabolism, and nucleic acid is the essential component for replication. An automaton that has only hardware but no software can exist independently and maintain its metabolism so long as it finds food to eat or numbers to crunch. By contrast, an automaton that has only software but no hardware can lead only a parasitic existence (e.g. viruses).

11.2 Cellular Automata

John von Neumann

The notion of cellular automata was put forward in the 1940s by Stanislas Ulam, a colleague of John von Neumann. What Ulam suggested to Neumann was to consider a digital programmable universe in which time is imagined as defined by the ticking of a cosmic clock, and space is a discrete lattice of cells, eachcell occupied by an abstractly defined very simple computer called a finite automaton. Very simple local rules determine the state of any cell at any discrete point of time. There are only a finite number of states available to a cell or automaton. These states could be, say, a few colours, or a few integers, or just ‘dead’ or ‘alive’, etc. At each tick of the digital clock, every automaton changes over to a new state determined by its present state and the present states of the neighbouring cellular automata (CA). The rules by which the state of each automaton changes at a given instant of digital time are the equivalent of the physical laws of the universe. There is thus a state-transition table, which describes how each automaton changes for each of the possible configurations of the states of the neighbouring cells.

11.3 John Conway’s ‘Game of Life’

A particularly popular example of CA is the Game of Life invented (around 1970) by John Conway. It provides a graphic demonstration of ‘artificial evolution’ because the fascinating evolving structures can be seen on a computer screen. One starts with a 2-dimensional lattice of square cells, each cell randomly taken to be either black or white. Let black mean that the ‘creature’ denoted by a cell is ‘alive’, and let white mean that the corresponding creature is ‘dead.’ Very simple local rules are introduced for how the cells will change from one time step to the next. For example, if a cell has two or three neighbours which are alive (i.e. black), then the cell becomes alive (black) if it was dead to start with, or remains alive if it was already so. If the number of live neighbours is less than two, the cell dies of ‘loneliness.’ And if the number of neighbours is more than three, again the cell dies, this time due to ‘overcrowding.’ Remarkable patterns emerge on the computer screen when this program is run. Every run is different, and it is not possible to predict or exhaust all possibilities, in keeping with what one would expect from a complex system. The live cells organize themselves into coherent and ever-changing patterns, like real creatures in Nature.

11.4 Wolfram’s ‘New Kind of Science’

What secret it is that allows nature seemingly so effortlessly to produce so much that appears to us so complex (?)

Stephen Wolfram

Studies on cellular automata got a big boost through the work of Stephen Wolfram. He introduced a whole new dimension to the study of complex systems. His monumental book on complexity (A New Kind of Science (NKS)), published in 2002, has been widely read, and continues to raise debate. I mentioned in Part 4 the notion of algorithmic irreducibility analyzed by Chaitin. That was information irreducibility. An information-irreducible digit stream is that for which there is no theory or model more compact than simply writing the string of bits directly. There is no program for calculating the string of bits that is substantially smaller than the string of bits itself. A somewhat different but related kind of irreducibility, namely computation-irreducibility was analyzed on a computer by Wolfram. (It is also called time-irreducibility because the longer a computation is, the more time it takes to perform it.) It pertains to physical and other systems for which there are no computational shortcuts, and for which the quickest way to see what a system will be like at a distant time is just to run the computer program (or the automaton) that is modelling the system. By contrast, a computationally reducible system is one which can be described by exact mathematical formulas that give the outcome at any chosen instant of time without working through all the time steps.

Wolfram is of the view that the complex phenomena we see in the world around us can be usually thought of as the running of simple computer programs. And the best and often the only way to understand these phenomena is by modelling them on a computer, rather than by working out the consequences of idealized and approximate mathematical models based on a set of equations. Wolfram’s idea of a ’simple program’ typically has the following ingredients: a set of transformation or operation rules (usually local rules); data to operate on; and an engine that applies the rules to the data. In a cellular automaton, the data enter only at the beginning of the computation (’initial conditions’), and the engine keeps applying the same deterministic rules to the outputs of its previous application of the rules. Extremely complex-looking patterns can be generated by any of a large number of simple programs investigated by Wolfram.

Shown here is a very simple 1-dimensional cellular automaton, which generates a complex-looking (nested) pattern. It consists of a row of squares, and each square can be either black or white. Starting from just one such row of squares (the top row in the figure), each time the system is updated, a new row of squares is created just below the previous row, following a simple rule. The simple rule operative in this figure says that a square in the new row should be black only if one or the other, but not both, of its vertically-above predecessor’s neighbours is black. Shown in a separate figure at the bottom is a graphical depiction of this rule, in which 8 blocks are drawn, corresponding to the 8 conceivable configurations of three neighbouring cells, each configuration determining the colour of the cell in the next row. Starting with a single black square in the top row of squares, this rule produces a complex pattern of nested triangles.

Here is another, beautiful, example of what a simple cellular automaton can generate:

Any process that follows definite rules can be regarded as a computation. Thus the CA can carry out computation, as can Turing machines, and many other systems in Nature. In computations carried out by humans on computers, the computer programs define the rules of computation. In Nature, the rules of computation are nothing but the laws of Nature.

The notion of a universal computer emerged from the work of Alan Turing in the 1950s, and this launched the computer revolution. It was demonstrated that it is possible to build universal machines with a fixed underlying construction, but which can be made to perform different computations by being programmed in different ways. With suitable programming, any computer system and computer language can be ultimately made to perform exactly the same set of tasks. Can at least some of the CA be universal computers? ‘Yes’ according to Wolfram. He describes the construction of a CA that can be a universal cellular automaton (UCA). The rule for this UCA is extremely simple. In fact it is a somewhat complex version of the so-called ‘rule 90′, which we have illustrated in the black-and-white figure above. My recent article should be consulted for more details on this.

Alan Turing

The immense number of CA examined by Wolfram has led to the formulation of his Principle of Computational Equivalence (PCE). According to this principle, almost all processes that are not ‘obviously simple’ correspond to computations that are of equivalent complexity. In other words, irrespective of the simple or complicated nature of the rules or the initial conditions of a process, any such process will always correspond to a computation of equivalent difficulty or sophistication.

The genesis of the PCE lies in the idea of computational universality: It is possible to construct universal systems that can perform essentially any computation, and which must therefore all be capable of exhibiting the highest level of computational sophistication. It does not matter how simple or complicated either the rules or the initial conditions for a process are; so long as the process itself does not look obviously simple (e.g. purely repetitive or purely ‘nested’), it will almost always correspond to a computation of equivalent sophistication. The PCE, though still under some debate, may have far-reaching consequences for science, and for much else.

Let us now see how the PCE rationalizes the rampant occurrence of computational irreducibility (or complexity) in Nature. For this we have to address the question of comparing the computational sophistication of the systems that we study with the computational sophistication of the systems that we use for studying them. The PCE implies that, once a threshold has been crossed, any real system must exhibit essentially the same level of computational sophistication. And this applies to our own perception and analysis capabilities also; according to the PCE they are not computationally superior to the complex systems we seek to observe and understand. Beyond a certain threshold, all systems are computationally equivalent in terms of complexity.

If predictions about the behaviour of a system are to be possible, it must be the case that the system making the predictions is able to outrun the system it is trying to make predictions about. But this is possible only if the predicting system is able to perform more sophisticated computations than the system under investigation. And the PCE does not allow that. Therefore, except for simple systems, no systematic predictions can be made about their behaviour at a chosen time in the future. Thus there is no general way to shortcut their process of evolution. In other words, most such systems are computationally irreducible.

As Wolfram emphasizes, the whole idea of doing science with mathematical formulas makes sense only for computationally reducible systems. For others there are no computational shortcuts; practically the only way of knowing a future configuration is to actually run through all the evolutionary time steps. And Wolfram’s NKS is ideally suited for that purpose. Exploiting the immense power of modern computers, one can generate a huge repertoire of the consequences of all sorts of simple programs as embodied in the corresponding CA. For understanding the basics of a given complex system observed in Nature, one can try to see if the observed behaviour pattern can be matched with any of the archived CA. If yes, then the simple program used for generating that particular CA pattern is the ‘explanation’ of the time or space evolution of the complex behaviour observed in the actual physical system under study (entailing a collapse in the degree of complexity of the system; cf. Part 5). Thus, rather than using CA to mimic or model the observed behaviour of complex systems, Wolfram advocates their use to reveal unknown aspects of the systems that they model. It remains to be seen how far this will turn out to be a useful way of doing science.

11.5 Does Randomness Rule Our Universe?

Is there a fundamental deterministic rule from which all else follows? Conventional wisdom says that randomness is at the heart of quantum mechanics, and because of this randomness the universe has infinite complexity. Wolfram suggests that this may or may not be so. According to him, there may be no real randomness; only pseudo-randomness, like the randomness produced by random-number generators in a computer. The computer generates these numbers by using mathematical equations, and what we get are actually deterministic sequences of numbers.

Wolfram gives the analogy of π = 3.1415926. . . Suppose you are given, not the whole equation, but only a string of digits coming from far inside the decimal expansion. It would look random, in the absence of complete knowledge. In reality it is only pseudo-random. Wolfram puts forward the viewpoint that, similarly, the randomness we see in the physical world may really be pseudo-randomness, and the physical world may actually be deterministic. It is simply that we do not know the underlying law, which may well be a simple CA for all we know. But there is also an important computational-irreducibility aspect to this scenario, as described above.

According to Wolfram, complexity in Nature follows from the existence of computational equivalence: ‘We tend to consider (a certain) behaviour complex when we cannot readily reduce it to a simple summary. If all processes are viewed as computations, then doing such reduction in effect requires us as observers to be capable of computations that are more sophisticated than the ones going on in the systems we are observing. But the PCE implies that usually the computations will be of nearly the same sophistication - providing a fundamental explanation of why the behaviour we observe must seem to us complex’ (cf. the website www.wolframscience.com).

There is a human or anthropic angle to the meaning of complexity. Let us go back to the equation π = 3.1415926… . It has only a small information content or degree of complexity: A small algorithm using the fact that π is given by the ratio of the circumference of a circle to its diameter can generate the entire information contained in this equation. But if we humans do not have knowledge about the entire equation, but are given only a string of digits coming from far inside the decimal expansion, then the degree of complexity is just about as large as the length of the string and can, in principle, be infinite. For us humans, the degree of complexity of a system depends on our knowledge about the system. As more knowledge is acquired by us, the degree of complexity may keep collapsing. Of course, this happens only for systems which are not irreducibly complex. If the complexity of a system is irreducibly or intrinsically large, our increasing knowledge about the system can have little effect on its degree of complexity.

11.6 Self-Reproducing Automata

In the late 1940s, Neumann got interested in the question: Can a machine be programmed to make a copy of itself? Can there be self-reproducing machines? To bring out the essence of self-reproduction, Neumann imagined a thought experiment. Consider a machine moving around on the surface of a pond. The pond contains all sorts of machine parts. Our machine is a ‘universal constructor‘ (rather like a universal computer); i.e., given a recipe for constructing any machine, it can search the pond for the right parts and construct the desired machine. In particular, it can construct a copy of itself if the requisite description is known to it.

But it is still not a self-reproducing machine, because the copy it has constructed of itself has no information about its own description for constructing another copy of itself. Neumann argued that for this to be possible, the original machine must have a ‘description copier’; i.e. a mechanism for duplicating the original description and for attaching this duplicate description to the new copy it is constructing of itself. The offspring will then have the wherewithal for a sustainable self-reproduction in this so-called Neumann universe.

For testing his thought experiment, Neumann used the CA suggested by Ulam. He proved that there exists at least one cellular-automaton pattern which can reproduce itself. The pattern he found involved a large lattice of cells, with 29 possible states for each cell. This was an important result because it meant that self-reproduction was possible in machines, and was not confined to living beings only.

Thus any self-reproducing system (living or nonliving) must play two roles: It should serve as an algorithm that can be executed during the copying and constructing process; and it should serve as a data bank that can be duplicated and attached to the offspring. These two predictions were confirmed for real-life systems in 1953 when Watson and Crick determined the structure of the DNA molecule. The DNA molecule not only serves as a data base for synthesizing various proteins etc., but it also unwinds and makes a copy of itself when the biological cell divides into two. Studies on CA help us understand life processes.

11.7 Concluding Remarks

As demonstrated by the work on CA, very simple local rules can lead to enormous amounts of complexity. This is why, although the laws of Nature are simple, we see so much complexity around us, including biological complexity.

The emergence of RNA and DNA molecules resulted in the replication aspect of self-reproduction in Nature. The next article in this series will elaborate on this when I discuss the origin(s) of life on Earth.

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

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COMPLEXITY EXPLAINED: 10. What is Life?

Vinod K. Wadhawan — Tue, 01 Dec 2009 05:52:14 +0000

(Note: All previous parts of Dr. Wadhawan’s series on complexity can be accessed through the Related Posts list at the bottom of this article.)

We live only to discover beauty. All else is a form of waiting. Khalil Gibran was happy describing life like this, but scientists have a lot of trouble defining it succinctly and comprehensively. It is not easy to give a crisp definition of life, just as it is not easy to define complexity in a context-independent and unique way. Perhaps there is no clear dividing line between life and nonlife. Nevertheless, the emergence of what many of us intuitively understand to be life marked a major milestone in the evolution of complexity in our world. I survey some of the scientific attempts at defining life, as a prelude to discussing the likely mechanisms for the origin of life in a future article in this series.

10.1 What is Life?

Here are a couple of descriptions of life. Eric Chaisson (2001) first:

But what is life? Like time, life is obvious to discern yet elusive to define. Although most biologists generally skirt the issue, we suggest that our very essence can be defined as follows: Life is an open, coherent, spacetime structure maintained far from thermodynamic equilibrium by a flow of energy through it - a carbon-based system operating in a water-based medium, with higher forms metabolizing oxygen.

Margulis and Sagan (2002) next:

Life does not exist in a vacuum but dwells in the very real difference between 5800 Kelvin incoming solar radiation and 2.7 Kelvin temperature of outer space. It is the gradient upon which life’s complexity feeds.

The origin of life (as also consciousness) is the most dramatic of all emergent phenomena in nonlinear open systems. But it has not been easy to define life the way we define so many other things in science. For every characteristic believed to define life, people have come up with an example from the world of the nonliving which also possesses that characteristic. In fact, as we make further progress in the development of sophisticated ‘artificial’ smart structures, including truly smart or intelligent robots, the distinction between the living and the nonliving will get more and more blurred. I have discussed these things in my book on smart structures, and also in an article on robots of the future.

Daniel Koshland is an ex-Editor-in-Chief of the prestigious magazine Science. He attended a conference focused on the vexing question of defining life. He recounts that, after considerable discussion, somebody formulated the essential characteristic of life as ‘the ability to reproduce.’ There seemed to be general consensus on this, till somebody said: ‘Then one rabbit is dead. Two rabbits - a male and female - are alive but either one alone is dead.’ Similarly, a mule must be a dead entity by this definition. Nevertheless, many of us still have, at least intuitively, an idea of what is living and what is nonliving. Koshland has come up, reluctantly, with the following short definition of life: A living organism is an organized unit, which can carry out metabolic reactions, defend itself against injury, respond to stimuli, and has the capacity to be at least a partner in reproduction. But he is happier giving a large set of criteria for deciding what constitutes life. He calls them the seven pillars of life, like the pillars of a Greek temple.

10.2 The Seven Pillars of Life

I asked the rose how long
was its life,
The bud heard and
softly smiled.

Mir Taqi Mir

Koshland’s (2002) seven ‘pillars’ of life are the essential thermodynamic and kinetic principles which enable a living system to operate and propagate. The mechanisms listed by him are with reference to life as we know it on our Earth. The same seven principles may involve other mechanisms for other forms of life, or for life elsewhere. The acronym PICERAS introduced by Koshland has seven letters, corresponding to the seven principles or pillars of life: program; improvisation; compartmentalization; energy; regeneration; adaptability; seclusion.

The first pillar of life is a program that describes the ingredients themselves, as well as the kinetics of the interactions among the ingredients. For life on Earth, this program resides in the DNA molecules that encode the genes.

Improvisation is the second pillar of life, and is to be distinguished from another pillar, namely adaptability, discussed below. Both involve a response to change. The difference is in time scales. Adaptability is about direct response to quick changes, and does not entail a change of the genetic program of the organism. Improvisation is about gradual change (evolution) in response to long-term changes in the environment.

A living organism depends on the reaction kinetics of its ingredients. The kinetics requires certain concentrations of the chemicals involved, and that requires confinement in a ‘container’. Therefore the third pillar of life is compartmentalization, namely the presence of a membrane or skin that confines the organism to a certain volume. In fact, large organisms have several compartments (organs), because the concentration requirements of different organs are different.

Energy is the fourth pillar of life on Earth. Without a steady input of energy, a living system would soon approach a state of equilibrium and death.

Regeneration (including reproduction) is the fifth pillar of life. There are always some thermodynamic losses and wear and tear as a living organism is sustained by the myriad chemical reactions going on inside it. Food and other intakes are one means of ensuring that the organism does not degenerate or degrade inexorably with time. Several organs of the human body (e.g. the heart) have a mechanism for tissue regeneration. However, over time, aging effects become too strong and the organism dies. To ensure that the species can still survive, Nature has evolved replication (cell division) and reproduction (which amounts to regeneration by starting all over again through the progeny) as an essential pillar of life.

A living organism may face a variety of sudden hazards. Adaptability is therefore the sixth pillar of life. For example, a living system must quickly move away from an environment that is too hot for its well-being and survival.

The seventh pillar of life listed by Koshland is what he calls seclusion. In a living cell there exist a large number of chemical reaction pathways, all simultaneously active. Natural evolutionary processes have ensured that the enzymes catalyzing the various reactions have specific shapes and reactivities, so that the different reactions do not interfere with one another. The specificity of an enzyme provides a high degree of seclusion to the relevant chemical reaction occurring in the living cell. Various feedback and feedforward mechanisms also ensure that the specificity is not completely unchangeable, but changes only under certain special signals.

The existence of many of Koshland’s pillars of life can be ultimately traced back to the DNA molecule, portions of which constitute the genes. DNA is a large molecule with very high information content. Life is information. Therefore there is a direct link between life and free energy (cf. Part 4). Without an input of free energy or negative entropy, all processes would tend to take a system towards a state of entropic death (cf. Part 6). Intake of food keeps an organism alive by providing negative entropy. As Szent-Györgyi (1957) said, ‘We need energy to fight against entropy’. The complex molecules constituting food are full of free energy or negative entropy, which is derived ultimately from the Sun.

10.3 Schrödinger and Life

Life is a partial, continuous, progressive and conditionally interactive self-realization of the potentialities of atomic electron states.

J. D. Bernal

The Nobel laureate Erwin Schrödinger made a profound discovery in 1927 by showing that the discrete energy states of matter are determined by wave equations. He became one of the founders of modern science, best known for the famous wave equation in quantum mechanics, named after him:

Here is an equivalent formulation of the Schrödinger equation:

Here Ĥ is the so-called Hamiltonian operator. The Hamiltonian H is defined as the sum of the kinetic and potential energies of the system. This equation determines all phenomena in our world, subject to the constraints of the first and the second laws of thermodynamics. How did Schrödinger arrive at this very basic equation? Here is Richard Feynman’s answer: ‘Where did we get that [Schrödinger's equation] from? It’s not possible to derive it from anything you know. It came out of the mind of Schrödinger.’

In 1943-1944 Schrödinger wrote a little book What is Life: The Physical Aspect of the Living Cell. This is how Roger Penrose described this book (in 1991): ‘… which, as I now realize, must surely rank among the most influential of scientific writings in this century. It represents a powerful attempt to comprehend some of the genuine mysteries of life, made by a physicist whose own deep insights had done so much to change the way in which we understand what the world is made of. … Indeed, many scientists who have made fundamental contributions in biology, such as J. B. S. Haldane and Francis Crick, have admitted to being strongly influenced by (although not always in complete agreement with) the broad-ranging ideas put forward here by this highly original and profoundly thoughtful physicist.’

It is important to realize that when Schrödinger wrote his book, the atomic structure of DNA was not known (it was determined later by Watson and Crick). I quote Freeman Dyson (1985): ‘Schrödinger’s book was seminal because he knew how to ask the right questions. The basic questions which Schrödinger asked were the following: What is the physical structure of the molecules which are duplicated when chromosomes divide? How is the process of duplication to be understood? How do these molecules retain their individuality from generation to generation? How do they succeed in controlling the metabolism of cells? How do they create the organization that is visible in the structure and function of higher organisms? He did not answer these questions, but by asking them he set biology moving along the path which led to the epoch-making discoveries of the subsequent forty years: to the discovery of the double helix and the triplet code, to the precise analysis and wholesale synthesis of genes, and to the quantitative measurement of the evolutionary divergence of species.’

How did Schrödinger define life? He avoided giving a direct definition of life, but highlighted an important property of it by invoking the idea of negative entropy, which I have outlined in Part 4 of this series of articles (also see Part 3 for a fuller description). He characterized living matter as that which stays alive (’evades the decay to equilibrium’) by feeding on negative entropy or negentropy. Karl Popper did not agree: ‘Now admittedly organisms do all this. But I denied, and I still deny, Schrödinger’s thesis that it is this which is characteristic of life, or of organisms; for it holds for every steam engine. In fact, every oil-fired boiler and every self-winding watch may be said to be “continually sucking orderliness from its environment”. Thus Schrödinger’s answer to his question cannot be right.’

10.4 Artificial Life

Life and artificial? Is that a contradiction in terms? Not at all. Christopher Langton is the originator of this subject. The term artificial life (AL) was coined by him around 1970. AL is ‘. . an inclusive paradigm that attempts to realize lifelike behaviour by imitating the processes that occur in the development or mechanics of life.’

In the field of AL, one uses computers to model the basic biological mechanisms of evolution and life. In abstracting the basic life processes, the AL approach emphasizes the fact that life is not a property of matter per se, but the organization of that matter. The laws of life must be laws of dynamical form, independent of the details of a particular carbon-based chemistry that happened to arise here on Earth. It attempts to explore other possible biologies in new media, namely computers and robots. The idea is to view life-as-we-know-it in the context of life-as-it-could-be.

In conventional biology one tries to understand life phenomena by a process of analysis: We take a living community or organism, and try to make sense of it by subdividing it into its building blocks. By contrast, AL takes the synthesis or bottom-up route. We start with an assembly of very simple interacting units, and see how they evolve under a given set of conditions, and how they change when the environmental conditions are changed. One of the most striking characteristics of a living organism is the distinction between its genotype and phenotype. The genotype can be thought of as a collection of little computer programs, running in parallel, one program per gene. When activated, each of these programs enters into the logical fray by competing and/or cooperating with the other active programs. And collectively, these interacting programs carry out an overall computation that is the phenotype. The system evolves towards the best solution of a posed problem. By analogy, the term GTYPE is introduced in the field of AL to refer to any collection of low-level rules. Similarly, PTYPE means the structure and/or behaviour that results (emerges) when these rules are activated in a specific environment.

What makes life and brain and mind possible is a certain kind of balance between the forces of order and the forces of disorder. In other words, there should be an edge-of-chaos existence. Only such systems are both stable enough to store information, and yet evanescent enough to transmit it. I shall return to this theme in the article on the origins of life.

Life is not just like a computation, in the sense of being a property of the organization rather than the molecules: Life literally is computation. And once we have made a link between life and computation, an immense amount of theory can be brought in. For example, the question ‘Why is life full of surprises?’ is answered in terms of the undecidability theorem of computer science, according to which, unless a computer program is utterly trivial, the fastest way to find out what it would do (does it have bugs or not) is to run it and see. This explains why, although a biochemical machine or an AL machine is completely under the control of a program (the GTYPE), it still has surprising, spontaneous behaviour in the PTYPE. It never reaches equilibrium.

The computational aspect of the AL approach invokes the theory of complex dynamical systems. Such systems can be described at various levels of complexity, the global properties at one level emerging from the interactions among a large number of simple elements at the next lower level of complexity. The exact nature of the emergence is, of course, unpredictable because of the nonlinearities involved.

Here are some websites devoted to artificial life and virtual worlds:

With the advent of artificial life, we may be the first creatures to create our own successors. . . If we fail in our task as creators, they may indeed be cold and malevolent. However, if we succeed, they may be glorious, enlightened creatures that far surpass us in their intelligence and wisdom. It is quite possible that, when conscious beings of the future look back on this era, we will be most noteworthy not in and of ourselves but rather for what we gave rise to. Artificial life is potentially the most beautiful creation of humanity.

Doyne Farmer and Alletta Belin

10.5 Concluding Remarks

Definition of life is problematic, particularly at the level of bacteria. This is partly linked to the question of what exactly we mean when we use the word ’species’ for a bacterium.

Fresh challenges to what we understand by the term ‘life’ will also arise when the fields of artificial life and super-intelligent robots come of age. I shall discuss artificial evolution in a separate article.

Until 1944 most scientists were of the view that genetic information was carried by the proteins of the chromosome. Schrödinger’s 1944 book What is Life?, apart from invoking negative entropy for the sustenance of life, introduced new concepts for the genetic code. It inspired Watson and Crick to investigate the gene, which led to their discovery of the double-helix structure of DNA. In their 1953 paper they wrote: ‘It has not escaped our notice that the specific pairing [of the two strands of DNA] we have postulated suggests a possible copying mechanism for genetic material.’ This sudden blaze of understanding laid bare the inside story of heredity, and of present-day life itself.

What is even more relevant for the stated objective of the present series of articles, Schrödinger argued that life is not a mysterious or inexplicable phenomenon, as some people believe, but a scientifically comprehensible process like any other, ultimately explainable by the laws of physics and chemistry.

The good life is one inspired by love and guided by knowledge.

Bertrand Russell

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

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COMPLEXITY EXPLAINED: 9. How Did Complex Molecules Like Proteins and DNA Emerge Spontaneously?

Vinod K. Wadhawan — Fri, 13 Nov 2009 22:25:41 +0000

Note: For previous parts to Dr. Wadhawan’s series on complexity check out the ‘Related Posts’ found at the bottom of this article.

How could the blind forces of Nature create large and highly information-laden molecules like DNA and proteins just by random processes? DNA carries information for the synthesis of proteins, but it requires the prior availability of certain protein molecules for performing its genetic duties. Such proteins help the double-helix DNA molecule to uncoil itself and split into two strands for replication purposes. Therefore, DNA and certain proteins must have emerged independently, by some efficient (and therefore reasonably likely) chemical processes. But how? The answer has to do with the chemical evolution of autocatalytic sets of molecules, which could consume energy-rich molecules and other precursors (’food’) to ‘reproduce’. These molecules were the predecessors of proteins and DNA etc., and thence of life.

9.1 Catalysis

Catalysis is a process that facilitates or speeds up a chemical reaction. Often, a chemical process may involve two or more intermediate reactions. A catalyst is a molecule that speeds up the production of an end product of the chemical process by participating in the intermediate reactions, but separates at the end of the chain of reactions, thus becoming available all over again for further catalysis. Often, a chemical reaction may almost never occur if no catalyst is present. Enzymes are examples of proteins that assist (i.e. catalyze) chemical reactions in biological systems.

Photosynthesis carried out by green plants in the presence of sunlight is another familiar example of catalysis. Chlorophyll is the catalyst here. Through a number of intermediate reactions, the net reaction is as follows:

Photons from the Sun make this reaction possible, and their energy gets stored in the form of chemical energy, resulting in an increase in the degree of complexity, or information content. Living organisms consume the energy stored in glucose, and some of it gets converted into more complex or more information-rich forms. Of course, not all photons from the Sun falling on our ecosphere get utilized like this. Most of them just dissipate their energy, with a corresponding increase of entropy.

9.2 Polymers

A polymer is a very long molecule, made up by covalent bonding among a large number of repeat units (monomers). There can be variations, either in that the bonding is not covalent everywhere, or in that not all subunits are identical. Examples of polymers and polymer solutions include plastics such as polystyrene and polyethylene, glues, fibres, resins, proteins, and polysaccharides like starch.

A homopolymer consists of a single type of repeat unit. A copolymer has more than one type of repeat units. A random copolymer has a random arrangement of two or more types of repeat units.

Sequenced copolymers are different from random copolymers in that, although the sequence of different subunits is not periodic, it is not completely random either. Biopolymers like DNA and proteins are examples of this. Their very specific sequence of subunits, ordained by Nature (through the processes of evolution), results in particular properties. Proteins in humans are sequenced copolymers made up of ~20 amino acids. The sequences of these amino acids in proteins give them the property to fold and self-organize into very specific 3-dimensional configurations.

How do short polymers form spontaneously in Nature? Recall the lock-and-key mechanism outlined in Section 8.4 (Part 8). Suppose a monomer has a shape and charge distribution such that another monomer can fit snugly into some part of it. There are random collisions among the monomers in a fluid medium, and usually they do not stick together, and simply bounce off after a collision. But once in a while the collision may be such that the two monomers have just the right orientation for a lock-and-key fitting. Then the chances of the two sticking together and forming a stable dimer are much larger. Dimers can lead to trimers, and so on, resulting in a polymer. Naturally, this can be a rather unlikely and therefore very slow process, and only short polymers can possibly form spontaneously in reasonable time.

9.3 Cell Biology

All tissues in animals and plants are made up of cells, and all cells come from other cells. A cell may be either a prokaryote or an eukaryote. The former is an organism that has neither a distinct nucleus, nor other specialized subunits or organelles. Examples include bacteria and blue-green algae. Unicellular organisms like yeast are eukaryotes. Such cells are separated from the environment by a semi-permeable membrane. Inside the membrane there is a nucleus and the cytoplasm surrounding it. Multicellular organisms are all made up of eukaryote-type cells. In them the cells are highly specialized, and perform the function of the organ to which they belong.

The nucleus contains nucleic acids, among other things. With the exception of viruses, two types of nucleic acids are found in all cells: RNA (ribonucleic acid) and DNA (deoxyribonucleic acid). Viruses have either RNA or DNA, but not both (but then viruses are not cells). Apart from the nucleus, an eukaryotic cell has mitochondria, ribosomes, and vacuoles. Plant cells also have chloroplasts. Mitochondria make energy out of food. Ribosomes make proteins. Vacuoles are used for storage of water or food. Chloroplasts use sunlight to create food by photosynthesis.

DNA is a long molecule that has the genetic information encoded in it as a sequence of four different molecules called nucleotides (adenine (A), thymine (T), guanine (G), and cytosine (C)). There is a double backbone of phosphate and sugar molecules, each carrying a sequence of the ‘bases’ A, T, G, C. This backbone is coiled into a double helix (like a twisted ladder). In this double-helix structure, base molecule A bonds almost always to base molecule T (via a weak hydrogen bond), and G bonds to C. The sequence of base pairs defines the primary structure of DNA.

DNA contains the codes for manufacturing various proteins. Production of a protein in the cell nucleus involves transcription of a stretch of DNA (this stretch is called a gene) into a portable form, namely the messenger RNA (or mRNA). This messenger then travels to the cytoplasm of the cell, where the information is conveyed to the ribosome. This is where the encoded instructions are used for the synthesis of the protein. The code is read, and the corresponding amino acid is brought into the ribosome. Each amino acid comes connected to a specific transfer RNA (tRNA) molecule; i.e. each tRNA carries a specific amino acid. There is a three-letter recognition site on the tRNA that is complementary to, and pairs with, the three-letter code sequence for that amino acid on the mRNA.

The one-way flow of information from DNA to RNA to protein is the basis of all life on Earth. This is the central dogma of molecular biology.

Three letters (out of the four, namely the bases A, T, C, G) are needed to code the synthesis of any particular protein. The term codon is used for the three consecutive letters on an mRNA. The possible number of codons is 64, and only 20 amino acids are processed by these codons. The linking of most of the amino-acid-triplets for synthesizing a protein can be coded by more than one codon.

There are ~60-100 trillion cells in the human body. In this multicellular organism (as also in any other multicellular organism), almost every cell (red blood ‘cells’ are an exception) has the same DNA, with exactly the same order of the nucleotide bases. The nucleus contains 95% of the DNA, and is the control centre of the cell. The DNA inside the nucleus is complexed with proteins to form a structure called chromatin.

The fertilized mother cell (the zygote) divides (self-replicates) into two cells. Each of these again divides into two cells, and so on. Before this cell division (mitosis) begins, the chromatin condenses into elongated structures called chromosomes. A gene is a functional unit on a chromosome, which directs the synthesis of a particular protein. As stated above, the gene is transcribed into mRNA, which is then translated into the protein. Humans have 23 pairs of chromosomes. Each pair has two non-identical copies of chromosomes, derived one from each parent.

During cell division, the double-stranded DNA splits into the two component strands, each of which acts as a replication template for the construction of the complementary strand. ‘Complementary strand’ means that for every A on the original template these is a T on the new strand; similarly, there is a C for every G, A for T, and G for C. At every stage, the two daughter cells are of identical genetic composition (they have identical genomes). In each of the 60 trillion cells in the human body, the genome consists of around three billion nucleotides.

9.4 Autocatalytic Sets of Molecules

Life depends on molecules of DNA, RNA, proteins, polysaccharides, etc. How did such large molecules get synthesized ’spontaneously’ from their building blocks, namely nucleic acids, amino acids, sugars, etc.? DNA and RNA have the crucial self-replication property. If we can explain their appearance on Earth, then self-replication and Darwinian natural selection can account for the emergence of simple life forms, as also their evolution into more and more complex life forms. Invoking random chance processes for the creation of large molecules like DNA, which are bearers of genetic information, is not a tenable idea because of the miniscule probability, and the correspondingly large time required for this to happen. In any case, there is no evidence that the origin of life on earth can be equated with the appearance of DNA.

The answer came through the idea of autocatalysis. Autocatalytic sets of molecules are those which can catalyse the synthesis of themselves. Autocatalysis requires that a given ‘factor’ (say A) should be able to convert a substrate or precursor B into a new factor of the same type: A + B → 2A + C. Melvin Calvin (1969) introduced the idea of autocatalysis as a mechanism for molecular selection, with implications for how life emerged on Earth.

There was little or no molecular oxygen (O₂) in the original atmosphere around the Earth. A variety of local energy sources were, of course, present (undersea hydrothermal vents; ultraviolet radiation; volcanic energy; radioactive nuclei; lightning; meteoric impacts). Under these conditions, amino acids, nucleotides, and other building blocks of the future living organisms got synthesized in the seas, and in the rock structures, and in the atmosphere around the Earth. Several energy-rich molecules like H₂S, FeS, H₂, phosphate esters, HCN, pyrophosphates, and thioesters, were also produced.

Thus, in this so-called primordial soup, namely a fluid in contact with rocks of various types, there existed small molecules of amino acids, sugars etc. Given enough time, some of them must have undergone random polymerization reactions of various types, producing short polymers. It is entirely possible that at least some of these end-products, with some side chains and branches hanging around, acted as catalysts for facilitating the production of other molecules which may also be catalysts for another chemical reaction. Thus: A facilitates the production of B, and B does the same job for C, and so on. Given enough time, and a large enough pool containing all sorts of molecules, it is quite probable that, at some stage a molecule, say Z, will get formed (aided by catalytic reactions of various types), which would be a catalyst for the formation of the catalyst molecule A we started with.

Once such a loop closes on itself, it would head towards what we now call self-organized criticality (and order). There will be more production of A, which will lead to more production of B, and so on. The plausibility advantage of this scenario visualised by Stuart Kauffman is that there is no need to wait for random reactions for the spontaneous formation of large molecules. And once a threshold has been crossed, the system is likely to inch towards the edge of chaos, and acquire robustness against destabilizing agencies.

Kauffman argued that this order, emerging out of molecular chaos, was akin to life: The system could consume (metabolize) raw materials, and grow into more and more complex molecules. It progressed into a situation where the forebears of DNA started appearing, with potential for replication. An era of chemical Darwinism or molecular Darwinism followed next, in which autocatalytic systems of molecules competed with one another for the limited supply of precursors and energy-rich molecules. These sets of autocatalytic molecules had at least three of the features of what constitutes life: They ‘ate’ the energy-rich molecules; they reproduced themselves; and they competed with other autocatalytic sets of molecules for survival.

9.5 Conclusions

The probability is next to nil that highly complex molecules like RNA, DNA and proteins got created spontaneously through purely random or chance processes. However, the nearly-impossible became possible, i.e. the unlikely set of events became likely, through the mechanism of autocatalysis. As John Avery has pointed out in his book Information Theory and Evolution (2003), ‘A notable feature of autocatalysis (apart from providing a credible mechanism for the origin of life) is that it has the seeds of natural selection at the molecular level: The precursor molecules and the energy-rich molecules are ‘food’. And the alternative autocatalytic systems compete for this supply of food. The efficient ones have a better chance of dominating and winning (through faster reproduction). Supply of free energy, of course, was/is the prerequisite for all this to become possible.’

Once a set of autocatalytic reactions had established itself, it went on incrementally evolving into still more complex sets of molecules. Chance events and/or new external conditions resulted in the emergence of a slightly more complex version of, say, one of the molecules in the autocatalytic set. A further round of chemical Darwinism and evolution of a new set of autocatalytic set of molecules followed. And so on, till molecules as complex as RNA, DNA and proteins emerged on the scene, which have life-sustaining and life-propagating properties.

This explanation is an important milestone in our quest for understanding in a rational manner the origin, or origins, of life on Earth. But what is life? I shall address this question in the next article in this series.

“The more we learn about the unbelievably complex, immensely varied, and yet simultaneously simple origin and development of life on earth, the more it looks like a miracle, and one that is still unfolding. The miracle of evolution.”

Sharon Moalem

Dr. Vinod Kumar Wadhawan is a Raja Ramanna Fellow at the Bhabha Atomic Research Centre, Mumbai and an Associate Editor of the journal PHASE TRANSITIONS.

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COMPLEXITY EXPLAINED: 8. Evolution of Chemical Complexity

Vinod K. Wadhawan — Fri, 30 Oct 2009 01:34:11 +0000

(Note: All previous parts in the Complexity Explained series by Dr. Vinod Wadhawan can be accessed through the ‘Related Posts’ listed below the article.)

How did life originate on Earth? Chemical or molecular evolution preceded the emergence of life. Under the influx of low-entropy energy from the Sun, and aided by the presence of certain rocks, atoms and molecules underwent chemical reactions resulting in the emergence of molecules of higher and higher information content or complexity. This article explains how this occurred.

8.1 From Atoms to Molecules

The chemical symbol H is used for an atom of hydrogen, which is the first element in the periodic table of elements. It has a nucleus, which is just a proton in this case, and there is an electron orbiting around the nucleus. The electron has a negative charge, exactly equal in magnitude to the positive charge of the proton. Taking this quantity as the unit of charge, we say that an H atom has a charge number 1 (Z = 1). Taking the mass of the proton as the unit mass, we say that H has a mass number 1 (A = 1). The electron is ~2000 times lighter than the proton.

Element number 2 in the periodic table is helium (chemical symbol He). There are two protons in its nucleus, and two electrons orbiting around the nucleus. There are also two neutrons in the nucleus. Neutrons are so called because they have no charge. The mass of a neutron is not very different from the mass of a proton. So, for the He atom, Z = 2, and A = 4.

Life on Earth is based on organic chemistry, i.e. the chemistry of the carbon atom, denoted by the symbol C. For this atom, Z = 6, and A = 12.

A molecule of hydrogen is denoted by the symbol H₂. It consists of two nuclei of hydrogen, and there are two electrons orbiting around them. Why does hydrogen ‘prefer’ to exist as H₂, rather than as H? Because H₂ is more stable that H. Why? Consider the two electrons of H₂. Quantum mechanics tells that they have no individuality, and are therefore indistinguishable. Let us consider any of them. Since positive and negative charges attract one another, the electron stays close (but not too close) to the two nuclei. [But for the Heisenberg uncertainty principle of quantum mechanics, the electrons of all the atoms would have gone right into their nuclei, and you and I would not be here, discussing chemical complexity!] Naturally, the positive charges on the two nuclei of H₂ are better than only one positive charge in H, when it comes to exerting an attractive force on the electron. Thus H₂ is more stable (it has a lower internal energy) than H because the former is a more strongly bound entity. Thus H atoms form H₂ molecules because by doing so the overall free energy gets reduced (the second law of thermodynamics demands that the free energy be as small as possible). Formation of H₂ from two atoms of H is an example of evolution of chemical complexity. More information is needed for describing the structure and function of H₂, than of H.

What is the nature of the bonding between the two atoms of H₂ or H-H? It is described as covalent bonding. Each of the two H atoms contributes its electron to the chemical bond between them, and the two electrons in the bonding region belong to both the nuclei.

Another kind of chemical bonding is the so-called electrovalent bonding (also called ionic bonding). It is the bonding that occurs between oppositely charged ions. Take sodium chloride (NaCl). For the Na atom, Z = 11, and for the chlorine atom, Z = 17. The laws of quantum mechanics are such that an atom of Na is more stable if it is surrounded by only 10 electrons, instead of 11. Similarly, Cl is more stable if it has 18 electrons, rather than 17. They can solve the problem together by getting readily ‘ionized’; i.e. an Na atom can become a positively charged ion Na⁺ by losing an electron (called the valence electron), and a Cl atom can become a negatively charged ion Cl^- by gaining an electron. The two oppositely charged ions can lower the potential energy (and therefore the free energy) by coming close to each other, thus forming an ionic bond between them.

The third important and generally strong type of bonding is metallic bonding. It occurs in metals like aluminium (Al), copper (Cu), silver (Ag), gold (Au), etc. Take the case of Al. For it, Z = 13. But like an atom of Na considered above, it is more stable if it has just 10 electrons around the nucleus. So Al atoms, when in the close vicinity of one another, lose their three valence electrons to a common pool, and these valence electron become the common property of all the Al ions. A lump of Al metal is held together by this cloud of negatively charged electrons, compensating for the positive charges on the Al ions.

8.2 The Hydrogen Bond and the van der Waals Bond

The covalent, electrovalent, and metallic bonds described above are the so-called primary bonds. They are strong bonds. Diamond, for example, consists of covalently bonded carbon atoms, and is a very hard material. In metals also the atoms are strongly bonded to one another, as are the atoms in a crystal of sodium chloride in which the electrovalent interaction dominates. There are a number of other types of bonds or interactions which are substantially weaker, but are very important for biological systems in particular, and soft matter in general. Particularly ubiquitous is the hydrogen bond. Take the example of water, H₂O or H-O-H. The oxygen atom forms covalent bonds with the two hydrogen atoms. Each such covalent bond (O-H) has two electrons associated with it, one coming from hydrogen and one from oxygen. The electron distribution around the two hydrogen nuclei in such a bond is not like that in a symmetrical bond like C-C in the structure of diamond. The oxygen nucleus has a charge number 8, which is much more than the charge number 1 of H, so it hogs a larger share of the electron charge cloud associated with the bond (we say the oxygen atom is very electronegative). This makes the nucleus of the hydrogen atom somewhat less shielded by the electron which was orbiting around it when there was no bonding of any kind. For similar reasons, the oxygen nucleus and its charge cloud of electrons are together a little more negative than they would be in an isolated atom of O. The end result is that the water molecule is like a little dipole. It has two positive ends and a negative end. All the water molecules are dipoles, so they tend to orient themselves such that a positive end (the hydrogen end) of one molecule points towards the negative end (the oxygen end) of another molecule. So we speak of hydrogen bonds, denoted in this example by O-H…O.

The most crucial aspect of the hydrogen bond in the evolution of chemical and biological complexity is that it is of intermediate strength, not as strong as the covalent bond, and yet not as weak as the so-called van der Waals bond (or the London dispersive bond). The van der Waals interaction is very weak, though always present between any two atoms. Quantum-mechanical fluctuations in the electronic charge cloud around an atom can result in a transient charge separation or dipole or multipole moment, and the electric field of this multipole induces a multipole moment on any neighbouring atom. This results in a small attraction between the two atoms.

The energy required to break a chemical bond is a measure of its strength. The melting point of a solid is an indicator of the strength of the weakest bonding in it. The covalent bond is the strongest, with a typical bonding energy of ~400 kilocalories (kcal). The electrovalent bond is typically half as strong as the covalent bond. The metallic bond shows a wide range of strengths, two extreme examples being the bonding in mercury on one extreme, and the bonding in tungsten on the other. The strength of a hydrogen bond is typically 14 kcal. And van der Waals bonding involves energies below 1 kcal. The most relevant fact for our purpose here is that the energy involved in hydrogen bonding is typically only ~10 times larger than the energy of thermal fluctuations, but is still much lower than the energy of a typical covalent bond. At typical temperatures at which biological systems exist, it is difficult for thermal fluctuations to break covalent bonds, but there is a fairly good chance that they can break hydrogen bonds.

8.3 The Hydrophobic and Hydrophilic Interactions

We have seen above that water is an aggregate of tiny dipoles. We say that it is a polar material. By contrast, there are a large number of ‘hydrocarbons’ which are nonpolar materials. [A hydrocarbon is a compound made predominantly of hydrogen and carbon atoms.] In contrast to the O-H bond in water, which is a bond with a dipole moment, the C-H bond in a hydrocarbon is largely nonpolar: The two electrons forming the C-H covalent bond are shared almost equally between C and H. Thus, a C-H bond hardly results in the creation of a dipole, and therefore it does not form a hydrogen bond with a water molecule. Now suppose we mix polar and nonpolar fluids. Segregation will occur. The nonpolar molecules will tend to huddle together because they cannot take part in the hydrogen bonding. They have a kind of ‘phobia’ for water molecules, and so we speak of the hydrophobic interaction. Since the hydrogen bond is of intermediate strength, the hydrophobic interaction is also of intermediate strength.

There are many types of organic compounds that are predominately of hydrocarbon structure, but have polar functional groups attached to them. Examples of this type are cholesterol, fatty acids and phospholipids. Such molecules have a nonpolar or hydrophobic end, and a polar or hydrophilic end. When put in water, they self-aggregate such that the hydrophilic ends point towards water, and the hydrophobic ends get tucked away, avoiding interfacing with water. This is why oil does not mix with water. By contrast, alcohol and water mix so readily that no stirring is needed; both are polar liquids. I forget the name of the king who said: ‘I do not care where the water flows, so long as it does not enter my wine!’

Beautiful self-assemblies like micelles, liposomes, and bilayer sheets may ensue because of the hydrophobic interaction.

8.4 Molecular Recognition and Self-Assembly

Let us go into some details of how the lowering of free energy occurs at the atomic scale. If two atoms are close to each other, they will bond together to form a molecule if the molecule has a lower free energy than that of the two separate atoms. Next, let us consider the possible bonding among molecules to form still larger assemblies (or ‘supramolecular aggregates‘). Things get more interesting now. The important concept of molecular recognition becomes operative here. Those types of molecules are likely to form assemblies which have a certain degree of complementarity. There are two types of complementarity to consider: That of lock-and-key-like shapes, and that of complementary charge distributions (remember, positive attracts negative). These complementarities, if present, enable two molecules to fit snugly into each other, thus lowering the overall potential energy, and thence the free energy. This is a more stable configuration because thermal fluctuations are less likely to knock the snugly-fitting molecules apart, and is the essence of chemical self-assembly in Nature. Self-assembly is like crystal growth, except that the end product may carry a lot more information; i.e. it is more complex.

The phenomenon of molecular complementarity was discovered by the Nobel Laureate Paul Ehrlich. As a student he was working on the newly discovered aniline dyes, which he used for staining biological cells. He found that each dye stained only a particular type of tissue or a specific species of bacteria, and not others. What happens is that the dye molecule moves around in the solution till it finds a binding site exactly fitting the pattern of atoms in one of its side chains. For stability, the complementarity of the ‘lock’ and the ‘key’ should be not only spatial, but also electrostatic; otherwise the specificity is not very strong: Not only the two shapes should be complementary, even the regions of positive excess charge on one molecule should be complementary to regions of negative excess charge on the other molecule. Here are some examples of spatial and charge complementarity in Nature:

The complementarity between the active site of an enzyme and the substrate of the enzyme.
The well-known ‘base-pair complementarity’ in DNA (deoxyribonucleic acid) and RNA (ribonucleic acid) strands. [I shall discuss this later.]
Self-assembly of viruses and subcellular organelles.
Receptors located on the surface of cells only bind a very limited number of substrates (often only one). The receptor is typically much more complicated (larger) than the substrate (hormone) that binds to it, as indicated in the accompanying sketch.

Supramolecular aggregates, normally formed under near-ambient conditions, do not involve covalent interactions usually. Instead, they are governed by weak, i.e. noncovalent or secondary, interactions (van der Waals; weak-Coulomb; hydrogen bond; hydrophobic; etc.). Because of this feature, the bonds in a supramolecular assembly at or near room temperature can get readily broken and re-formed, in a time-reversible manner, until the system has found its most stable configuration. Reversibility of bonding is a very important feature of self-assembly through molecular recognition.

Biological and other soft materials can self-assemble into a variety of shapes, and over a whole range of length scales. There is usually some amount of water present, and the most important factor mediating self-assembly is the hydrophobic interaction. Incidentally, self-assembly per se is a far more ubiquitous phenomenon than just molecular self-assembly. Some examples are: crystals; liquid crystals; bacterial colonies; beehives; ant colonies; schools of fish; weather patterns; even galaxies.

Self-assembly may be either static or dynamic. The former occurs in systems which are in local or global equilibrium, and which do not dissipate energy (e.g. crystals). Dynamic self-assembly is more relevant from the point of view of evolution of complexity, and always involves dissipation of energy. Here are some examples: oscillating and reaction-diffusion reactions; weather patterns; galaxies.

Weak interactions, with energies comparable to thermal energies, ensure that the bonds can be made and unmade reversibly, until the lowest-energy ordered configuration has been reached.

Growth of molecular crystals is an example of this. Reversibility also implies that the growing (self-assembling) system is close to equilibrium at all times.

8.5 Evolutionary Drug Designing

As a small digression, I want to mention here the use of the lock-and-key idea for designing drugs. Very often, for a drug to be effective, its molecular structure should be such that it can fit snugly into a relevant cleft in a protein molecule. In more general terms, drug activity is obtained through the binding of one molecule, i.e. the ‘ligand’, to the pocket of another, usually larger, molecule called the receptor. In their binding conformations, the molecules exhibit geometric and chemical complementarity, both of which are necessary for successful drug activity.

It can be very expensive to actually synthesise all those trial drugs and test their compatibility with the cleft in the protein molecule. Therefore, computers are used to carry out what is called ‘evolutionary computing‘. The computer code generates billions of random drug molecules, which it tests against the cleft in the protein. One such imaginary molecule may contain a site which matches one of, say, six sites on the cleft. This molecule is then ’selected’ (it has an ‘evolutionary advantage’), and a billion variations of it are created, and tested using a suitable ‘fitness test’. And so on to the next generations of trial molecules, till the best drug shape is obtained. I shall discuss such ‘artificial evolution’ in a future article, after introducing the basics of biological evolution. As Kevin Kelly (1994) said: ‘Evolutionary breeding of drugs is the future of biotechnology.‘

8.6 The Tobacco Mosaic Virus

I consider here the example of the tobacco mosaic virus (TMV) to illustrate the hazy, perhaps nonexistent, line between life and nonlife. Any virus (including TMV) typically has an RNA core and a protein coating. It is possible to separate these two components, and purify and store them in the laboratory. At any later time the components can be mixed and incubated, and the TMV gets reconstituted by self-assembly. The reconstituted TMV thus not only comes back to ‘life’, it can even reproduce itself if placed on a tobacco leaf!

8.7 We Owe Our Lives to the Hydrogen Bond

Life and its evolution depend on the hydrogen bond. This bond is much weaker than the covalent bond, and yet strong enough to sustain self-assembled biological structures, enabling them to withstand the disintegrating influences of thermal fluctuations and other perturbations. Hydrogen bonding, and the associated hydrophobic interaction, has the right kind of strength to enable superstructures to self-assemble without the need for irreversible chemical reactions. There is a strong element of reversibility associated with these weak interactions, enabling the spontaneous making and breaking of assemblies until the lowest-free-energy configuration has been attained.

8.8 Self-Organization

The amount of information contained in organized or complex matter is very high. This information is distributed among the shapes of the component molecules, and in the interaction patterns among them. The build up of this information involves a succession of stages: molecular recognition; self-assembly; self-organization; and chemical adaptation and evolution. We have already considered the first two. Let us now focus on self-organization.

Lehn (2002) defined self-organization as the ’spontaneous but information-directed generation of organized functional structures in equilibrium conditions’. The necessary information (’coding’) for self-organization is contained in the molecular-recognition and self-assembly proclivities of the component molecules. This coding also determines how the self-assembled edifice self-organizes into a functional structure in equilibrium. For a recent survey of the various types of coding for self-organization, see my book Smart Structures: Blurring the Distinction between the Living and the Nonliving (2007).

Self-organization is a far more ubiquitous phenomenon than something at just the molecular level. Here are some examples:

A laser is a self-organized system. Under properly engineered conditions, photons spontaneously group themselves into a configuration in which they all move in phase, resulting in a powerful laser beam.
A hurricane is a self-organized system. The steady influx of energy from the Sun draws water from the oceans, as well as drives the winds. Mild tropical winds may grow into an organized configuration of a hurricane when some critical threshold is crossed.
A living cell is a self-organized system, which organizes itself all the time, depending on the environment.
An economy is a self-organizing system. The demand for goods and services, as also the demand for labour, constantly organizes the economy in a spontaneous way, without any central controlling authority.

8.9 Chemical Adaptation and Evolution

Given a set of conditions, molecules in a system tend to self-organize so as to minimize the overall free energy. This is chemical adaptation. Now suppose this set of conditions changes. This is very likely, in fact inevitable, because we are dealing with an open system. A further round of self-organization must occur, governed as always by the second law of thermodynamics. This is chemical evolution. Moreover, the set of changing conditions, i.e. the changing environment experienced by the molecules, need not necessarily be that external to the set of molecules. Even internal changes in the molecular system present a changed environment to every member of the set. And molecular configurations are changing all the time. Thus, chemical adaptation and evolution occurs in an open system of molecules (including our ecosystem) all the time.

One can draw analogies with Darwinian evolution to see if ‘natural selection’ (i.e. molecular selection) and ’survival of the fittest’ also occurs in chemical evolution. The answer is ‘yes’ because when the resources are limited, there is competition among the alternative molecular-reaction pathways, and only the fittest pathways can survive so far as consumption of precursor molecules and energy-rich molecules is concerned. Such considerations aroused special interest for explaining the origin of life-sustaining molecules. Some pioneering work in this direction was done by Melvin Calvin (1969), who introduced the idea of autocatalysis as a mechanism for molecular selection. I shall consider autocatalysis in the next article.

8.10 Concluding Remarks

The lock-and-key idea is crucial for explaining the evolution of molecules and molecular assemblies of increasing complexity in Nature. Two molecules may ordinarily interact only weakly, but a snug fitting of portions of the two molecules can lead to a much stronger degree of cohesion between them, because they ‘touch’ or attract each other at many points.

Another crucial factor for the chemical evolution of complexity is the reversibility of the non-covalent bonding between molecules; reversibility is the key to self-assembly. And once a stable self-assembly has got created (through molecular trial and error), generally there is no ‘looking back’. The overall large cohesive energy has a stabilizing effect. But there can still be a looking forward: If the environment changes, the self-assembled system can respond (i.e. adapt) by again exploiting the reversible nature of its non-covalent interactions. This is chemical evolution. As we shall see in due course, chemical evolution has led to biological evolution. And as I have said many times, evolution and emergent properties are the hallmarks of complexity.

I conclude by saying a few words about vesicles, which provide a good, even dramatic, example of self-organization in a nonliving complex system. Vesicles are spherical supramolecular assemblies separating an aqueous interior volume from the external solvent by means of lipid bilayers. They are also called liposomes, and are quite similar to micelles (see figure above). Given the right conditions, lipids can self-assemble into giant vesicles the size of biological cells. The basic driving force for their self-assembly is the hydrophobic interaction. As vividly described by Menger and Gabrielson (1995), vesicles can mimic the living cell in many ways, even though they are not living entities:

When a giant vesicle, which happens to have a smaller vesicle inside it, is exposed to octyl glucoside, the smaller vesicle can pass through the outer membrane into the external medium (’birthing’). The resulting injury to the membrane of the host vesicle heals immediately. Addition of cholic acid, on the other hand, induces a feeding frenzy in which a vesicle grows rapidly as it consumes its smaller neighbours. After the food is gone, the giant vesicle then self-destructs (a case of ‘birth, growth, and death’). Such lifelike morphological changes were obtained by using commercially available chemicals; thus these processes should be assigned to organic chemistry, and not to biology or even biochemistry.

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COMPLEXITY EXPLAINED: 7. Cosmic Evolution of Complexity

Vinod K. Wadhawan — Fri, 16 Oct 2009 04:24:30 +0000

Our universe is believed to have begun with the Big Bang, 10-15 billion years ago. Its degree of complexity at and soon after that moment was next to nil. Then why and how has the cosmic complexity gone on increasing? In fact, it is increasing exponentially fast. The explanation can be traced ultimately to the fact that the universe has been expanding all the time.

7.1 Quantum Mechanics

All phenomena are governed by the laws of quantum mechanics. Quantum theory has been remarkably successful in explaining a vast range of observations. It is also highly counterintuitive. We accept it because there is no better theory for understanding natural phenomena. In any case, there is no reason why the laws of Nature should not be counterintuitive to humans. There is nothing special about us, except that we possess intelligence and consciousness. In the history of the cosmos, we emerged on the scene very recently, whereas the laws of Nature have been there all the time.

The electron microscope provides a good example of the counterintuitive behaviour of fundamental particles like electrons. Let us first consider the conventional optical microscope we use for obtaining a magnified view of small objects. We cannot observe an object in total darkness, so we must shine some light on it. The object scatters the light in all directions. The pattern of the scattered light carries information about the shape and other properties of the object. Some of this diverging scattered light is intercepted by a lens of the microscope. The lens bends the waves of scattered light so that they can recombine or ‘interfere’ with one another, and produce an image of the object in the so-called ‘image plane’, located somewhere between the lens and its focal point.

Suppose we want to go on increasing the ‘resolving power’ of the optical microscope. That is, we want that even when two particles are located very close to each other, they should still be seen as separate in the image produced by the microscope. Naturally, the question of the wavelength of the light used for illuminating the object becomes relevant. The smaller this wavelength is, the greater is the resolving power. But how small a wavelength can we use and still obtain an image of the object? There is a practical limit. Suppose we want to use X-rays, instead of visible light, for illuminating the object. X-rays and visible light are both electromagnetic radiation; they differ only in wavelength, X-ray wavelengths being typically 5000 times shorter than those in the visible part of the electromagnetic spectrum. This presents a serious practical difficulty. It is not possible (at least not easy) to find a lens which can bend X-rays sufficiently to meet and interfere and form an image. It is easier to solve this problem by using electrons instead of visible radiation.

Yes, electrons. There is an inverse relationship between the speed of an electron and the wavelength associated with it. Electrons are charged particles, and we can use high electric fields to accelerate them to high velocities, with a correspondingly short wavelength. But what about the lens needed to bend the high-speed electrons after they have been scattered by the object we want to view? No problem; just use electric fields to do the bending. All this is what is actually done in an electron microscope. The fact that electron microscopy is a reality is proof that this line of reasoning must be correct.

So an electron in motion has a wavelength associated with it. But an electron is also a particle, with a definite ‘rest mass’. This wave-particle duality, though counterintuitive, is an important feature of quantum theory. Similarly, a beam of light (in fact, radiation of any wavelength) is not just a wave, but also has a particle aspect. Particles (or quanta) of light are called photons. Interestingly, Einstein got the Nobel Prize, not for his work on the theory of relativity, but for work which confirmed the particulate nature of radiation.

Now, a wave is not something you can specify in terms of location at a point in space (unlike a particle). A wave has nonlocal character, with an amplitude and a phase at every point in space. That leads to another counter-intuitive aspect of quantum mechanics: Since a particle has a wave aspect also, it can be everywhere is space (with varying probabilities, of course). Thus, we cannot specify the position of a particle with complete certainty. This conclusion is a blow to the traditional (classical mechanics) worldview of things. Classical mechanics can be deterministic, but not quantum mechanics. In classical (or Newtonian) mechanics, one can not only specify the position and momentum of a particle with infinite precision, one can also determine (through the equations of motion) the position and momentum of that particle at any time in the future and in the past. Quantum indeterminism has been the subject of many philosophical discussions. In quantum mechanics, we can speak only in terms of probabilities, and not certainties.

7.2 The Heisenberg Uncertainty Principle

Let us return to the issue of having to shine some probing radiation (photons or electrons or whatever) on an object for viewing it. The quantum of the probing radiation has a certain momentum and energy, so it disturbs what we are trying to observe when it impinges on it. This is an unhappy situation indeed, but totally unavoidable: The act of observing an object disturbs it.

And how much is the disturbance? To answer that question, let us move away from just microscopy, and say that we want to determine both the position and the momentum of the object. As a simple case, suppose the object is at rest, so its momentum is zero. The act of impinging it with even one quantum of the probe (e.g. a photon) will impart it some momentum, and also disturb its initial position. Suppose we want to determine the position very accurately. That would require a probing photon of very small wavelength. But such a photon also has more energy compared to a longer-wavelength photon, so the disturbance or uncertainty in the momentum will be larger. The converse is also true: If we try to reduce the uncertainty in our knowledge of the momentum by using a larger-wavelength probe, the uncertainty in the measurement of the position will be larger. This tradeoff between the uncertainty in position and the uncertainty in momentum is captured neatly by the celebrated Heisenberg uncertainty principle of quantum mechanics. It says that there is a lower limit to the uncertainty with which we can specify both the position and the momentum of a particle. Suppose the uncertainty in position is Δx, and the uncertainty in momentum is Δp_x, then the product Δx.Δp_x must always be greater than a small but nonzero universal value. The uncertainties in position and momentum mean that unpredictable quantum fluctuations can occur in their values within the limits prescribed by the Heisenberg principle.

The quantum-mechanical uncertainty becomes a dominant effect only when we are dealing with entities of very small sizes and masses. If we are dealing with a heavy object, it would normally have a large size also. Therefore, bombarding a few photons for determining its position and momentum will hardly cause any relative disturbance to the values of these parameters. This is one example of how quantum mechanics merges seamlessly with classical mechanics when we are dealing with macroscopic objects in everyday life.

7.3 Our Universe

There have been competing theories about the origin of our universe, and whether the universe indeed has a beginning and an end. Edwin Hubble made the crucial observation during the 1920s that the universe is expanding. This meant that, if we imagine a time reversal, there must have been a moment when all the contents of the universe were at one point, a so-called ‘singularity‘. The so-called Big Bang occurred at that moment, and the universe has been expanding ever since. The Big Bang theory was proposed in 1930 by Georges Lemaître, and developed by other physicists, notably George Gamow. The theory implies that the universe had a definite beginning and has a finite age.

Fred Hoyle, Hermann Bondi, Thomas Gold, and Jayant Narlikar formulated the alternative ’steady-state’ theory of the universe in 1948. This theory implies an infinitely old universe, with no ‘beginning.’ The Bell Labs scientists Arno Penzias and Robert Wilson discovered in 1965 the cosmic background radiation that Gamow had predicted to be a consequence of the Big Bang model. The observation of this radiation, a relic of the early universe, delivered a body-blow to the steady-state model of the universe. However, the last word has not been said yet about what is the correct model for the universe. The steady-state-universe model has much to commend itself.

The two pillars of modern physics are the quantum theory, and the general theory of relativity. Quantum mechanics has been remarkably successful in understanding the physics of very small objects (like electrons), and the general theory of relativity deals with very large distances and masses for which gravitation becomes a dominant interaction. Isaac Newton was the first to understand gravity as an attractive force between bodies that depends only on their masses and the distance between them. Newton’s theory was extended by Einstein’s general theory of relativity. The new theory treated gravity as a distortion of space rather than a force between bodies.

The moment of the Big Bang was a singularity because it involved very small dimensions and very large gravitational forces. So a good theory for explaining this scenario must merge quantum mechanics with general-relativity theory. In other words, we need a theory of quantum gravity. Such a theory still eludes us, although there has been considerable progress through the work of Stephen Hawking and others. As argued by Hawking and Penrose, Einstein’s general theory of relativity is only an incomplete theory. It cannot tell us how the universe started off, because it predicts that all physical theories, including itself, break down at the beginning of the universe.

Hawking has come up with idea of ‘a universe without boundaries’, as discussed in a somewhat nontechnical language in his famous book The Universe in a Nutshell. In 3-dimensional space, the surface of a sphere is a good example of a ‘universe’ without boundaries from the vantage point of a creature constrained to exist only on the surface of the sphere; there is no beginning or end to the surface of the sphere. In the words of Hawking: ‘It is perhaps ironic that, having changed my mind, I am now trying to convince other physicists that there was in fact no singularity at the beginning of the universe - as we shall see later, it can disappear once quantum effects are taken into account’. In the Hartle-Hawking model, the universe is finite but has no boundary in imaginary time. Imaginary time is real time multiplied by the square root of minus one ((-1)^1/2). Mind boggling stuff indeed.

7.4 The Big Bang

The singularity at the moment of the Big Bang was of such small spatial dimensions that quantum-mechanical effects in general, and the Heisenberg uncertainty principle in particular, were extremely dominant. There is a viewpoint that the universe was born as a quantum fluctuation. The quantum fluctuation in momentum (Δp) or kinetic energy permitted by the Heisenberg principle (because of the vanishingly small spatial dimensions Δx at the moment of the singularity) was large enough to account for the immense amount of the energy in the universe. Space and time were strongly twisted in the beginning. Space itself exploded, its dynamics explained for later moments of time by Einstein’s geometrical laws of general relativity.

How can energy be created out of nothing, and how is it continuing to increase as the universe expands? Here I am on uncertain ground, as the experts do not yet agree on what really happened. Apart from what I have said above (which may be debatable), here is a possible answer, given by Seth Lloyd (2006) in his book Programming the Universe: ‘Quantum mechanics describes energy in terms of quantum fields, a kind of underlying fabric of the universe, whose weave makes up the elementary particles - photons, electrons, quarks. The energy we see around us, then - in the form of Earth, stars, light, heat - was drawn out of the underlying quantum fields by the expansion of our universe. Gravity is an attractive force that pulls things together. . . As the universe expands (which it continues to do), gravity sucks energy out of the quantum fields. The energy in the quantum fields is almost always positive, and this positive energy is exactly balanced by the negative energy of gravitational attraction. As the expansion proceeds, more and more positive energy becomes available, in the form of matter and light - compensated for by the negative energy in the attractive force of the gravitational field.’ Lloyd emphasizes the complementary roles of energy and information in the cosmic evolution of complexity: ‘Energy makes physical systems do things. Information tells them what to do.’

7.5 Nature Abhors Gradients

To understand the cosmic evolution of complexity, it is helpful to take note of the fact that ‘Nature abhors gradients’. This is usually not stated as a law of science, but is a clear consequence of the ‘official’ laws of thermodynamics. It provides a different perspective to why evolutionary progress occurs. We all know how difficult it is to maintain vacuum in a vessel. Nature abhors vacuum, and tends to fill up empty space with whatever molecules happen to be around. What is really happening is that there is a gradient of pressure, and this gradient tends to get destroyed in an irreversible manner, in accordance with the second law of thermodynamics. In fact, the second law itself is nothing but a statement about the spontaneous destruction of gradients like thermal gradients, pressure gradients, concentration gradients, etc.

So we can generalize and say that Nature abhors gradients of all types. In particular, it may be noted that when a system is pushed away from a state of thermodynamic equilibrium by an influx of energy and/or matter, a gradient is created. As discussed in Part 3 and Part 6, if the departure from equilibrium is not too large, Nature restores equilibrium by destroying the gradient. But if the departure from equilibrium is too large, then the system is unable to return to the old configuration of equilibrium, and must seek a new steady state or equilibrium state. What is more, since the departure from equilibrium is large, the system tends to find more efficient ways of destroying gradients, and this results in pattern formation and emergent phenomena or structures so characteristic of complexity. Just think of the whirlpool, or, if you are familiar with such things, the regular pattern created by a so-called Bénard instability.

7.6 The Cosmic Evolution of Complexity

Chaisson (2001) identifies three eras in the cosmic evolution of complexity. In the beginning there was only radiation, with such a high energy density that there was hardly any structure or information content in the universe; it was just pure energy. As the universe cooled and thinned, a veritable phase transition, or bifurcation in the phase-space trajectory occurred, resulting in the emergence of matter coexisting with radiation. This marked the start of the second era, in which a high proportion of energy resided in matter, rather than in radiation. The third era was heralded by the onset of ‘technologically manipulative beings‘.

As the very hot plasma after the Big Bang expanded, it also cooled. The temperature was ~10³² K, 10^-43 seconds after the Big Bang. Gravitation appeared at this stage. Around 10^-34 seconds later, the temperature was ~10²⁷ K, and matter appeared in the form of quarks, leptons, gauge bosons, and several other elementary particles. ‘Antimatter’ also appeared. The appearance of matter can be attributed to quantum fluctuations in the density of the universe, amplified by the effects of gravity. Even a miniscule increase in local density could attract more matter towards it, with a corresponding decrease in the surrounding density.

Around 10^-10 seconds later, the electro-weak interaction split into the electromagnetic interaction and the weak interaction (another symmetry-breaking phase transition or bifurcation, like so many in the cosmic evolution, with a concomitant increase in the degree of complexity). Around 10^-5¹² K. This is when the quarks formed the protons and the neutrons, and the antiquarks formed antiprotons. The collisions between protons and antiprotons left behind mostly protons, as well as photons. Around one second later, collisions among electrons and positrons occurred, leaving behind mostly electrons. Around one minute later, with temperature ~10⁹ K, neutrons and protons could coalesce, resulting in nuclei like those of helium, lithium, and the (heavy) isotopes of hydrogen. seconds later, the temperature had fallen to ~10

About ten million years after the Big Bang, enough cooling had occurred to fill the universe with a mist of particles, containing mostly hydrogen and some helium, as also some elementary particles, including neutrinos, some electromagnetic radiation, and perhaps some other, unknown, particles. The universe was just cold, dark, and formless at that stage. Then some quantum-mechanical primordial fluctuations in the densities of the particles gave rise to a clumping of some of the particles, rather like the nucleation that precedes the growth of a crystal from a fluid. The presence of such clumped particles suddenly brought the gravitational forces into prominence, leading to a cascading effect. Portions of the mist began collapsing into huge swirling clouds. Over a period of a few hundred million years, huge galaxies, each containing billions of young stars of various sizes, formed and began to shine. The formless darkness of the initial period was gone.

The large superstars among these were strongly bright spheres, the brightness coming from the fusion of hydrogen and helium in their interiors, made possible by the prevailing extreme temperatures and pressures. This is how the heavier elements got formed in the interiors of these large stars. The emergence of heavier elements by the process of nuclear fusion continued steadily until the element iron started forming. The iron nucleus is the most stable of them all. Iron cannot fuse with one or more nucleons and release radiative energy of nuclear fusion. Its presence acts as a poison for the nuclear fusion process. Thus the appearance of iron marked the beginning of the end of the available nuclear fuel, and therefore the end of the life of the star. In due course, the smaller stars simply ceased to shine, shrinking into cold and dead entities.

But a very different fate awaited the larger stars. No longer able to sustain their size because of the progressively decreasing processes of nuclear fusion of elements, they began to collapse under their immense gravitational pull. A rapid change occurred in their interiors. Under the immense squeezing generated by the collapse, the iron-atom core imploded. This resulted in a new state of matter as the electrons and the protons in the atoms were squeezed together. The dominant process of interaction now was the electro-weak interaction, in which protons and electrons reacted to produce neutrons and electron-neutrinos. The collapse led to a compression of the star to an extremely dense ball of pure neutron matter. Concomitantly, the neutrino cloud burst outwards, resulting in an explosion (the supernova explosion) of the outer shell of the star. This is how the newly synthesized elements (up to the atomic number for iron), residing in the outer shell of the star, were scattered into the universe, accompanied by a brilliant flash of light.

A consequence of such supernova explosions (which still occur from time to time, and light up the galaxies with brilliant flashes of light) was the emergence of clouds of dust and gas and the debris containing heavy elements. These clouds encircled the galaxies in spiralling arms. The intensity of the explosions was so high that elements heavier than iron were also produced and scattered into space.

In the outer portions of the spirals occurred a condensation of the dust and the clouds and the debris, resulting in the formation of the second generation of (smaller) stars (including our Sun), as also planets, moons, comets, asteroids, etc. Our solar system was formed when the universe was ~9 billion years old. In the initial period, our Earth underwent several violent upheavals (bombardment by comets and meteors, as also huge earthquakes and volcanic eruptions). By the time the Earth was ~2.5 billion years old, its continents had formed. Life appeared in due course.

7.7 Why is There so Much Complexity in the Universe?

At the moment of the Big Bang, the information content of the universe was probably zero, assuming that there was only one possible initial state and only one self-consistent set of physical laws. Existence of information means that there are alternatives available; e.g. 0 or 1. If there were no alternatives to the initial state of the universe, then it did not require any bits of information to describe it. Soon after time and space began, the quantum fields contained very little information and energy to begin with. Thus, in the beginning, the effective complexity, the logical depth, and the thermodynamic depth (cf. Part 5) were all zero, or nearly zero. This view is consistent with the fact that the universe emerged out of nothing.

As the early universe expanded, it pulled in more and more energy out of the quantum fabric of space and time. Under continuing expansion, a variety of elementary particles got created, and the energy drawn from the underlying quantum fields got converted into heat, meaning that the initial elementary particles were very hot and increasing in number rapidly, and therefore the entropy of the universe increased rapidly. And high entropy means that the particles require a large amount of information to specify their coordinates and momenta. This is how the degree of complexity of the universe grew in the beginning.

Soon after that, quantum fluctuations resulting in density fluctuations and clumping of matter made gravitational effects more and more important with increasing time. The extremely large information content of the universe results, in part, from the quantum-mechanical nature of the laws of physics. The language of quantum mechanics is in terms of probabilities, and not certainties. This inherent uncertainty in the description of the present universe means that a very large amount of information is needed for the description. This is just another way of saying that the present degree of complexity of the universe is very large.

But why does the degree of complexity go on increasing? In Part 5 we introduced the metaphor of a monkey typing away randomly on the keyboard of a computer. We concluded that Ockham’s razor ensures that short and simple programs are the most likely to explain natural phenomena, which in the present context means the explanation of the evolution of complexity in the universe. The quantum-mechanical laws of physics are the ’simple programs’, as well as the computer. But what is the equivalent of the monkey, or rather a large number of monkeys, injecting more and more information and complexity into the universe by programming it with a string of random bits? According to Seth Lloyd (2006), ‘quantum fluctuations are the monkeys that program the universe‘.

The current thinking is that the universe will continue to expand, and that it is spatially infinite. But the speed of light is not infinite. Therefore, the causally connected part of the universe has a finite size, limited by what has been called the ‘horizon‘ (Lloyd 2006). The quantum computation being carried out by the universe is confined to this part. Thus, for all practical purposes, the part of the universe within the horizon is what we can call ‘the universe’. As this universe expands, the size of the causally connected region increases, which in turn means that the number of bits of information within the horizon increases, as does the number of computational operations. Thus the expanding universe is the reason for the continuing increase in the degree of complexity of the universe.

7.8 Concluding Remarks

The ever-present expansion of the universe is a necessary cause (though perhaps not a sufficient cause) for all evolution of complexity, because it creates gradients of various kinds: ‘Gradients forever having been enabled by the expanding cosmos, it was and is the resultant flow of energy among innumerable non-equilibrium environments that triggered, and in untold cases still maintains, ordered, complex systems on domains large and small, past and present’ (Chaisson 2006). The ever-present expansion of the universe gives rise to gradients on a variety of spatial and temporal scales. And, ‘it is the contrasting temporal behaviour of various energy densities that has given rise to those environments needed for the emergence of galaxies, stars, planets, and life.’

In the grand cosmic scenario, there was only physical evolution in the beginning, and it prevailed for a very long time. While the physical evolution still continues, the emergence of life started the phenomenon of biological evolution. The ‘Nature abhors gradients’ way of looking at the evolution of complexity has been particularly well articulated by Lynn Margulis and Dorion Sagan (2002) in their book Acquiring Genomes: A Theory of the Origins of Species: ‘Although it is difficult to say why the universe is so organized, the measured universal expansion since the Big Bang of space continues to provide a “sink” (a place) into which stars as sources can radiate: A progenitive cosmic gradient, the source of the other gradients, is thus formed by cosmic expansion. For the foreseeable future the geometry of the universe’s expansion continues to create possibilities for functionally creative gradient destruction, for example, into space and in the electromagnetic gradients of stars. Once we grasp this organization, however, life appears not as miraculous but rather another cycling system, with a long history, whose existence is explained by its greater efficiency at reducing gradients than the nonliving complex systems it supplemented.’

It is perhaps a sobering thought that we seem so inconsequential in the Universe. It is even more humbling at first - but then wonderfully enlightening - to recognize that evolutionary changes, operating over almost incomprehensible space and nearly inconceivable time, have given birth to everything seen around us. Scientists are now beginning to decipher how all known objects - from atoms to galaxies, from cells to brains, from people to society - are interrelated.

Eric Chaisson, Cosmic Evolution

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