Claim: Living organisms have goals and purposes – the product of billions of years of evolution
Reply: The living is and cannot be the product of evolution. Evolution depends on DNA replication - which was not extant prior self replicating cells emerged on the scene.
It is here, in the way living systems arrange information into organized patterns, that the distinctive order of life emerges from the chaos of the molecular realm. Scientists are just beginning to understand the power of information as a cause that can actually make a difference in the world.
Now for an arresting postscript. There’s another make of walker called dynein that, in what seems like a fit of designer madness, walks the other way along the same fibres as kinesin.
Life has devised some amazingly clever and efficient ways to read the RNA output and fix up goofs.
A different demonic motor springs into action when the cell divides and DNA is duplicated. Called DNA polymerase, its job is to copy from one DNA strand into another, daughter molecule, which again is built one letter at a time as the motor crawls along. It typically moves at about 100 base pairs per second and, like RNA polymerase, it also operates at close to thermodynamic perfection.
E. coli generate only about six times the heat of the theoretical minimum limit set by thermodynamics, so they are almost as efficient on the cellular level as they are on the nano-machine level. How can we explain the astonishing thermodynamic efficiency of life?
Organisms need to have perfected the art of storing and processing information or they would quite simply cook themselves to death with waste heat.
A typical mammalian cell may contain up to 10 billion proteins, which places them on average only a few nanometres apart. Every nanomachine is continually buffeted by the impact of high-speed water molecules, which make up much of the cell’s mass.
The cell as a whole is a vast web of information management.
Once the chain of amino acids is completed, it may be modified by yet other proteins in many different ways. It must also fold into the appropriate three-dimensional structure, assisted by yet more proteins that chaperone the flexible molecule during the folding process. All this exquisite choreography has to work amid the thermal pandemonium of the cell.
biological information is not merely acquired, it is processed. Living organisms are not just bags of information: they are computers. It follows that a full understanding of life will come only from unravelling its computational mechanisms.
The patterns of information control and organize the chemical activity in the same manner that a program controls the operation of a computer. Thus, buried inside the ferment of complex chemistry is a web of logical operations. Biological information is the software of life. Which suggests that life’s astonishing capabilities can be traced right back to the very foundations of logic and computation.
Living organisms seem to be actual selfreproducing machines.
An important point von Neumann stressed is that it is not enough for a UC simply to make a replica of itself. It also has to replicate the instructions for how to make a UC and insert those instructions into the freshly minted replica; otherwise, the UC’s progeny would be sterile.
The crucial insight von Neumann had is that the information on the tape must be treated in two distinct ways. The first is as active instructions for the UC to build something. The second is as passive data simply to be copied as the tape is replicated. So the logical organization of a living cell closely mirrors that of a von Neumann self-replicating machine.
Whatever the minimal complexity for life may be, there is no doubt that even the simplest known life form is already stupendously complex. Indeed, life’s complexity is so daunting that it is tempting to give up trying to understand it in physical terms.
There is generally no simple connection between a gene, or a set of genes, and a biological trait at the level of the organism. Many traits emerge only when the system as a whole is taken into account, including entire networks of genes in interaction, plus many non-genetic, or so-called epigenetic, factors that may also involve the environment
In a paper entitled ‘Life, logic and information’, Nurse heralded a new era of biology.Increasingly, he pointed out, scientists will seek to map molecular and biochemical processes into the biological equivalent of electronic circuit boards:
Focusing on information flow will help us to understand better how cells and organisms work … We need to describe the molecular interactions and biochemical transformations that take place in living organisms, and then translate these descriptions intothe logic circuits that reveal how information is managed … Two phases of work are required for such a programme: to describe and catalogue the logic circuits that manage information in cells, and to simplify analysis of cellular biochemistry so that it can be linked to the logic circuits … A useful analogy is an electronic circuit. Representations of such circuits use symbols to define the nature and function of the electronic components used. They also describe the logic relationships between the components, making it clear how information flows through the circuit. A similar conceptualization is required of the logic modules that make up the circuits that manage information in cells
Claim: Importantly, the information patterns in living things are not random. Rather, they have been sculpted by evolution for optimal fitness, just as have anatomy and physiology. Of course, humans cannot directly perceive information, only the material structures
Reply: The problem with this claim is, that the origin of life cannot be explained with evolution.
Imagine if we tried to understand how a computer works by studying only the electronics inside it. We could look at the microchip under a microscope, study the wiring diagram in detail and investigate the power source. But we would still have no idea how, for example, Windows performs its magic. To fully understand what appears on your computer screen you have to consult a software engineer, one who writes computer code to create the functionality, the code that organizes the bits of information whizzing around the circuitry. Likewise, to fully explain life we need to understand both its hardware and its software – its molecular organization and its informational organization.
Many chemical circuits are controlled by genes ‘wired’ together via chemical pathways to create features like feedback and feed-forward – familiar from engineering, but the details can be messy. To give the flavour, let me focus on a very basic property of life: regulating the production of proteins. Organisms cleverly monitor their environment and respond appropriately.
Making the right amount of a particular protein is a delicately balanced affair that needs to be carefully tuned. Too much could be toxic; too little may mean starvation.
Although I have focused on transcription factors, there are many other complex networks involved in cellular function, such as metabolic networks that control the energetics of cells, signal transduction networks involving protein–protein interactions, and, for complex animals, neural networks. These various networks are not independent but couple to each other to form nested and interlocking information flows.
The existence of so many regulatory chemical pathways enables them to fine-tune their behaviour to play ‘the music of life’ by responding to external changes with a high degree of fidelity, much as a well-tuned transistor radio can flawlessly play the music of Beethoven.
Reorganization of chromatin is under the control of a network of threads, or microtubules. Thus, whole sets of genes may be silenced or activated mechanically, either by keeping them ‘under wraps’ (wrapped up, more accurately) or by unravelling the highly compacted chromatin in that region of the chromosome to enable transcription to proceed.
There are only ten nodes and twenty-seven edges in the Schizosaccharomyces pombe yeast cell cycle network. First order of business is to confirm that the patterns are non-random. More precisely, if you just made up a network randomly with the same number of nodes and edges, would the twinkling lights differ in any distinctive way from Mother Nature’s yeast network? To answer that, my ASU colleagues Hyunju Kim and Sara Walker ran an exhaustive computer study in which they traced the ebb and flow of information as it swirls around the yeast network.20 This sounds easy, but it isn’t. You can’t follow it by eye: there has to be a precise mathematical definition of information transfer The upshot of their analysis is that there is an elevated and systematic flow of information around the yeast network well in excess of random.
The special role of these four genes has earned them the name ‘the control kernel’. The control kernel seems to act like a choreographer for the rest of the network, so if one of the other nodes makes a mistake (is on when it should be off, or vice versa), then the control kernel pulls it back into line. It basically steers the whole network to its designated destination and, in biological terms, makes sure the cell fissions on cue with everything in good order.
Somehow, information etched into the one-dimensional structure of DNA and compacted into a volume one-billionth that of a pea unleashes a choreography of exquisite precision and complexity manifested in three-dimensional space, up to and including the dimensions of an entire fully formed baby. How is this possible?
How do liver cells gather in the liver, brain cells in the brain, and so on – the cellular equivalent of ‘birds of a feather flock together’? Most of what is known comes from the study of the fruit fly Drosophila. Some of the morphogens are responsible for causing undifferentiated cells to differentiate into the various tissue types – eyes, gut, nervous system, and so on – in designated locations. This establishes a feedback loop between cell differentiation and the release of other morphogens in different locations. Substances called growth factors (I mentioned one called EGF earlier in this chapter) accelerate the reproduction of cells in that region, which will alter the local geometry via differential growth. This hand-wavy account is easy to state, but not so easy to turn into a detailed scientific explanation, in large part because it depends on the coupling between chemical networks and information-management networks, so there are two causal webs tangled together and changing over time. Added to all this is growing evidence that not just chemical gradients but physical forces – electric and mechanical – also contribute to morphogenesis.
Electrical pre-patterning appears to guide morphogenesis by somehow storing information about the three-dimensional final form and enabling distant regions of the embryo to communicate and make decisions about large-scale growth and morphology.
Where is the morphological information stored in these creatures, and how is it passed on between generations? Obviously, the information is not in the genes, which are identical. The DNA alone does not directly encode shape (anatomical layout) or the rules for repairing that shape if damage occurs.
One way forward is to imagine that there is some sort of ‘information field’ permeating the organism, which, after Levin and his collaborators have adulterated it, somehow embeds details about the large-scale properties of the monster-in-waiting, including its three-dimensional form.
The way Levin expresses it, there is a pre-existing ‘target morphology’ that guides a variety of shape-modulating signals and is stored, interpreted and implemented by a combination of chemical, electrical and mechanical
processes acting in concert:
A ‘target morphology’ is the stable pattern to which a system will develop or regenerate after perturbation. Although not yet understood mechanistically, regeneration ceases when precisely the right size structure has been rebuilt, indicating a coordination of local growth with the size and scale of the host.
Epigenetic information processing and control is distributed throughout the cell (and perhaps beyond the cell too). It is global, not local, as is the case with the cellular analogue of von Neumann’s supervisory unit
Nature can only work with the variants it has, and a fundamental question is how those variants arise. If something works well, random changes are likely to make it worse, not better.
Theodor Dobzhansky wrote:
‘The most serious objection to the modern theory of evolution is that since mutations occur by “chance” and are undirected, it is difficult to see how mutation and selection can add up to the formation of such beautifully balanced organs as, for example, the human eye.’
Starving bacteria, Rosenberg discovered, can switch from a high-fidelity repair process to a sloppy one. Doing so creates a trail of damage either side of the break, out as far as 60,000 bases or more: an island of selfinflicted
vandalism. Rosenberg then identified the genes for organizing and controlling this process. It turns out they are very ancient; evidently, deliberately botching DNA repair is a basic survival mechanism stretching back into the mists of biological history. By generating cohorts of mutants in this manner, the colony of bacteria improves its chances that at least one daughter cell will accidentally hit on the right solution. Natural selection does the rest. In effect, the stressed bacteria engineer their own high-speed evolution by generating genomic diversity on the fly.
Human DNA would suffer devastating mutational damage, estimated at an overall 1 per cent copying error rate per generation, without all the in-house, high-tech proofreading, editing and error correcting which reduces the net
mutation rate to an incredible one in 10 billion.