Ch 12: final extract and conclusion

A new and better model for understanding the science of life

The left hemisphere’s take on change is that it must consist of the rapid succession of one static element by another. This may look like flow, but it is in fact its opposite: a concatenation – a chain of elements that are themselves static, and distinct – which do not merge one with another. There are always gaps, however infinitesimal, between the entities we imagine as things. This stasis and disconnexion is nowhere to be found in flow.

Discreteness and flow are both aspects of reality, but the latter is ontologically prior to the former. What quantum field theory has to say about this I will postpone to Chapter 24; for now I will content myself with quoting David Tong, Professor of Theoretical Physics at Cambridge:

At a fundamental level is nature discrete or continuous? I see no evidence whatever for discreteness. All the discreteness we see in the world is something which emerges from an underlying continuum … Quanta are emergent … they are not built into the heart of Nature.’^[1]

Analysis never stops until it reaches unchanging particles: if these parts took to changing (and they do), it would have to split them up in their turn.^[2] It can end only once separation and stasis have been achieved. The difference between flow and the rapid substitution of static elements may sound no big deal, especially since the invention of ciné film, and subsequent digitised media. But it changes a whole host of things.

Living beings cannot be understood simply by reducing them to an aggregation of parts. Knowledge of the parts can often bring useful information, but that is not the same thing. And in today’s science establishment what is most highly rewarded is seeing minutiae in the whole – the more minute the better – not the whole in which the minutiae inhere. Both exercises are important: according to Schrödinger, much more the latter.

Models are very powerful predictors of the path science will take, and the findings it will encounter. If it is true that each science progresses from an initial conception of its relevant phenomena as things – substances such as phlogiston or vital fluid – to one in which they are seen as processes; and if it is true that, as Mark Bickhard asserts, process is now ‘the dominant language of science’, exceptions remain. What are they? Oddly, as he points out, ‘the sciences and philosophies of mind and persons. Here substance and structural views are still dominant.’^[3] This is just as David Bohm predicted.

Trying the stream of life model would be worth it, just to see what it revealed, even if it didn’t look like a much closer fit – as it seems to me it clearly does. The stream of life model is more capacious than, and is able to accommodate, the strengths of the machine model for what it is worth, where it helps: it maintains what Dupré calls ‘as much analytical sharpness as reality allows’, while giving full acknowledgment to fluidity and flexibility.^[4]

But seeing life as a stream is also a model, just as the seventeenth-century Cartesian machine is. And a model is only truer than another if it explains more of the phenomena we see – or have failed to see, because of the tyranny of the formerly ruling model. The new model, too, can be jettisoned, when the time comes, once it has done its work. But to judge it fairly does require deploying it, and mentally inhabiting it for long enough to see what difference it makes to the observed world. In other words, you won’t even be in a position to see what it has to offer without first making a leap of imagination.^[5] Dismissing it just because it isn’t the model you now hold is senseless. In a well-known formulation, ‘you don’t see something until you have the right metaphor to perceive it’.^[6]

In 2004, Carl Woese, whose work revolutionised microbiology, wrote of the complacency he saw in biology at that point in history:

Look back a hundred years. Didn’t a similar sense of a science coming to completion pervade physics at the nineteenth century’s end – the big problems were all solved; from here on out it was just a matter of working out the details? Déjà vu! Biology today is no more fully understood in principle than physics was a century or so ago. In both cases the guiding vision has (or had) reached its end, and in both, a new, deeper, more invigorating representation of reality is (or was) called for. A society that permits biology to become an engineering discipline, that allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.^[7]

And he concludes, in an image that should resonate in the mind of the reader: ‘molecular biology could read notes in the score, but it couldn’t hear the music’.^[8]

Let us return to our overarching context, which is, after all, the examination of science’s claims on truth. I am a believer in Pragmatism. Science is Pragmatist, or should be; not merely pragmatic. The machine model may be useful at times, but it is not accurate, because, as I have tried to show, it does not answer to what we know of organisms from observation, examination, and experiment – in other words from science as she should be practised. This trade-off between truth and utility is also that between the dispositions of, respectively, the right (more truthful, but more complex) and left (less truthful, but simpler) hemispheres. Unfortunately, science’s claims on truth are compromised if they are hijacked by the left hemisphere refusal to acknowledge the limitations of its chosen model of re-presentation (here, the machine model). And, as I say, it is not as if this hijack has no consequences in the world of every day: the consequences are of the most serious nature.

How would a paradigm shift, such as the one I believe we need, change the way we thought of ourselves – influence, in other words, how we answered Plotinus’s question?

In the first place, it would help us, quite simply, to avoid viewing ourselves as machines, the most crippling possible distortion of what it means to be a human being. Believe it or not, in the Royal Institution Christmas Lectures for 2013 (‘the UK’s flagship science series’, according to its website, aimed at aspiring young scientists), an Oxford biology lecturer used the very specially human act of two people gazing at one another to impress on her indeed impressionable audience of 11–18 year olds that ‘you’re amazing, we’re all amazing, because we’re all hugely complicated machines…’^[9] Perhaps it had slipped her mind that machines are not social beings; that they don’t have consciousness, feelings, personality, will or individuality; that they have no appreciation of music, dance, poetry, art or nature; that they have no sense of humour and they do not have the ability to change their minds, to sorrow over the past or project a delighted future. And that’s not even taking into account the far more complex issues entailed in human consciousness, including imagination, morality, creativity, the capacity for spiritual awe, and an allegiance to beauty, truth and goodness. And in case that should sound the slightest bit rarefied, I’d like to mention that they don’t have bodies either. And they do not die. It is true, of course, that you’re amazing, we’re all amazing; but it’s precisely because we are not just hugely complicated machines. If we carry on talking like this to the aspiring young, we will get no better scientists than we will deserve.

As biochemist Addy Pross writes, this dire process begins at the cellular level:

There is a growing awareness of an elephant in the room. Life is more complicated than a representation provided by a string of 3 billion letters. The gap between the elucidation of the human genome sequence and understanding the significance of that sequence is cavernous. The uncovering of more and more structural and mechanistic information within the living cell hasn’t clarified what life actually is … Our attempts to view biological systems as mechanical–materialistic machines have failed dismally.^[10]

And those attempts are encouraged by the fact that scientific research into the human being, and specifically into the brain, is almost exclusively carried out using the model, and the language, of the machine. Neurospeak is replete with references to ‘wiring’, ‘circuitry’, ‘modules’, ‘switches’, ‘signals’, ‘data banks’, ‘inputs’, ‘outputs’, and to the brain ‘encoding’, ‘computing’, and having ‘mechanisms’ of every conceivable kind: and no-one knows better than I do from personal experience how hard such terms are to avoid, given the existence of the culture. Yet Berry is surely right to say that ‘we should banish from our speech and writing any use of the word “machine” as an explanation or definition of anything that is not a machine. Our understanding of creatures and our use of them are not improved by calling them machines.’ It is to be expected that the hermetic response of the left hemisphere will be that living creatures are machines. But we do not know that: we only see the mechanical aspects revealed by our model, and I hope in the course of this book to give the reader some insight into how limiting and damaging this habit has become. The language we use is hugely important: it determines what we can understand. ‘Over the past fifty years or so’, wrote David Mermin in 1990,

scientists have allowed the conventions of expression available to them to become entirely too confining. The insistence on bland impersonality and the widespread indifference to anything like the display of a unique human author in scientific exposition, have not only transformed the reading of most scientific papers into an act of tedious drudgery, but have also deprived scientists of some powerful tools for enhancing their clarity in communicating matters of great complexity. Scientists wrote beautifully through the nineteenth century and into the early twentieth. But somewhere after that, coincident with the explosive growth of research, the art of writing science suffered a grave setback, and the stultifying convention descended that the best scientific prose should sound like a non-human author addressing a mechanical reader.^[11]

Sadly, these words are yet truer today.

In the second place, we have been taught to regard ourselves as selfish, and the natural world out of which we emerge as essentially a field of ruthless competition. This has truth. But it is very much a half-truth. Its opposite is also true. We are moral beings, capable of selflessness, fulfilled through our interconnectedness with one another and the natural world at large. We are not atomistic. And the story of life on earth is not therefore one of competition only. It is at least as much, and arguably more, as I have already suggested, a story of co-operation and collaboration. In fact collaboration might be seen as, sensu stricto, ‘one of the central characteristics of life’.^[12]

‘Complexity’, says Pross,

facilitates the replicative process … in [a recent] experiment it was found that a single replicating RNA molecule was relatively ineffective at self-replication, but a pair of similar RNA molecules, neither self-replicating but each helping the other replicate, worked way better. Conceptually one can think of that step as the first on a thousand-mile journey toward that biological cell – a highly complex cooperative entity, highly effective at making more of itself.^[13]

Multicellular organisms appear to have arisen from single cells numerous times independently in the course of evolution.^[14] This alone implies that collaboration is a fundamental element in the evolution of life. According to Dupré and microbiologist colleague Maureen O’Malley, ‘single animal or plant cells are only truly alive when they are collaborating with other cells … in a great variety of ways.’ ^[15] Though it is part of our idea of living beings that they are autonomous entities, the very integrity of their function as a whole, on which such an idea of autonomy depends, itself already involves a host of other organisms.^[16]

This does not at all rule out competition: in fact the combination of competition and co-operation is just what collaboration means – and needs. As always, the forces for division must be balanced by those for union, and vice versa, though ultimately the two tendencies must be fruitfully unified, not divided. A collaborative team requires difference, not sameness, of types, strengths and roles; and every society – a form which life from its earliest origins approximates, even within the single cell – contains, and must contain in order to be healthy, elements of both difference and sameness. Communities of cells, according to Dupré and O’Malley,

exhibit well-defined cell organization and a functional division of labour that includes specialised cell-to-cell interactions, the suppression of cellular autonomy and competition, metabolic collaboration, combined defence and attack strategies, and the coordination of movement, growth and reproduction.^[17]

And, as they go on to say,

whatever sense we might try to make of the Dawkinsian idea of selfish genes, molecular replication is always, and has always been from the pre-cellular molecular community to the present, the achievement of ensembles of molecules, not of individual molecules.^[18]

And the entities that are seen to compete are already collaborations of ‘many different lineage-forming entities.’^[19]

They see competition as a necessary stage in collaboration, ‘transitional’ rather than ‘terminal’:

temporarily competitive wholes will exhibit a strong tendency ultimately to compete most successfully by engaging in new levels of collaboration with similar or different entities … our concept of collaboration assumes no sharp boundary between selfish and cooperative interactions, something surely to be expected if the former is inclined to evolve into the latter.^[20]

I would also add here: on the whole, evolution has led to greater genuine collaboration, greater empathy, greater beauty and greater harmony. When reductionists want to demonstrate the brutality of nature, they are generally reduced – fittingly – to citing creatures such as the ichneumon wasp that lays its eggs in the larvae or pupae of another species, and the fascinating and, I agree troubling, lives of parasites (on which see Chapter 27). I don’t say that all is sweetness and light as we ascend the evolutionary tree, far from it: man, in particular, is a deeply flawed being – as well to remember that, when we pretend to be able to peer into the nature of the cosmos and find ready answers. But the very reason we are capable of deliberate harm is bound up with our special power to do deliberate good. (Social evolution, on the other hand, is another matter: I do not agree with Stephen Pinker that we are becoming better human beings. Though I fully understand his argument, I believe we are becoming less fully human, as we sacrifice what we could have learnt from attending to our right hemisphere’s understanding of the world.)

Conclusion

In the longer term many of our present assumptions about Nature, in both the broader and the more restricted sense of that term, may need to be revised, which is why at the largest possible scale it is still ultimately unpredictable: ‘Surprise is inherent in the structure of the world’, says Lee Smolin. ‘Nature can throw us surprises for which no amount of knowledge would have prepared us.’^[21] Jacob Bronowski once commented: ‘The physical scientists have more fun. Their theories are more eccentric; they live in a world in which the unexpected is everyday.’^[22] But all is unexpected if we could just see it. It’s just that physicists have learnt to expect the unexpected; many biologists are still in a preceding phase, in which the unexpected is dismissed, covered up, or simply not seen at all.

Things are changing, if slowly. Even one of the world’s most influential professors of mainstream bioengineering, Jay Keasling at Berkeley, together with three of his distinguished colleagues, wrote in a collaborative review published in the high-impact journal Cell:

An open question is whether biology is genuinely modular in an engineering sense or whether modularity is only a human construct that helps us understand biology … In the context of biological engineering, it is still an open question whether abstraction is a useful tool or a necessary evil.^[23]

It is both a useful tool, and it is an evil – how necessary depends on what you want science to do for you. Ultimately, it is not that the machine model does not have a role. There does not have to be just one model here. But, crucially, as in all hemispheric differences, one is more widely and fundamentally applicable than the other. Low-level analyses versus portrayal of the whole: it’s not an either-or matter, but if you want to understand the whole, it’s clear which is more important. Mechanism is a perfectly useful way of looking at tiny details in a complex picture so as to be in a position to manipulate them. There it has had spectacular successes. But we mustn’t be blinded by them. The problem is thinking that the same thinking will help you understand the whole: it can’t. ‘In its prime each system is a triumphant success’, wrote Whitehead: ‘in its decay it is an obstructive nuisance. The transitions to new fruitfulness of understanding are achieved by recurrence to the utmost depths of intuition for the refreshment of imagination.’^[24] That is what we now need.

Haldane wryly noted of Pasteur that his work

appealed most strongly to those who desired to stress the contrast between mind and matter … It is perhaps not quite irrelevant that he worked in his later years with half a brain. His right cerebral hemisphere had been extensively wrecked by the bursting of an artery when he was only forty-five years old; and the united brain power of the microbiologists who succeeded him has barely compensated for that accident.^[25]

Because understanding is more than providing a low-level description, the similarity to the understanding of a text is not accidental. To quote Lewontin again: ‘An organism is less like a machine than it is like a language whose elements … take unique meaning from their context’. Analyses of individual words and their possibilities of meaning can be an essential first step: without a knowledge of the words, we cannot grasp the whole. But at the same time, it is only the meaning of the whole that gives the individual words their full and proper significance. This, in a nutshell, is the truth that has for so long been ignored within biology.

As always, what the left hemisphere sees must serve the more inclusive vision of the right hemisphere, not come to dominate it. It makes an invaluable servant, but a tyrannical master. Failure to observe this principle leads to the neglect and obscuration of the most fundamental insights into the question posed by Plotinus, addressing which, according to Schrödinger, is the only justification for science: ‘and we – who are we, anyway?’

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^[1] Tong 2016.

^[2] See Bergson 1911a (8).

^[3] Bickhard 2009 (553).

^[4] Dupré 2017b.

^[5] Whitehead 1929a (4).

^[6] Robert Stetson Shaw; quoted in Gleick 1987 (262).

^[7] Woese 2004 (173).

^[8] Woese op cit (175).

^[9] Dr Alison Woollard, Royal Institution Christmas Lectures, 2013.

^[10] Pross 2012 (114–5).

^[11] Mermin 1990 (xi–xii).

^[12] Dupré & O’Malley 2009 (14). See also Wohlleben 2017.

^[13] Pross 2014.

^[14] Kaiser 2001.

^[15] Dupré et al, op cit (10).

^[16] ibid (11).

^[17] ibid. See also Cohn 1877; Shapiro 1998; Aguilar, Vlamakis, Losick et al 2007; Kolenbrander 2000; Lazazzera 2005; Fuqua, Winans & Greenberg 1994; Cho, Jönsson, Campbell et al 2007; and Crespi 2001.

^[18] Dupré et al op cit, further citing Segré & Lancet 2000.

^[19] Dupré et al, op cit (14).

^[20] ibid (emphasis added).

^[21] Smolin 2013 (xvii).

^[22] Bronowski 1958 (65).

^[23] Way, Collins, Keasling et al 2014.

^[24] Whitehead 1933 (159).

^[25] Haldane 1968 (2–3).