Life's Greatest Secret

by Matthew Cobb

Nonetheless, cybernetics was important in helping Jacob and Monod understand the data from their operon experiments and thereby contributed to our understanding of gene regulation. In 1970, Monod attempted to explain the organisation of living systems in a book entitled Chance and Necessity. Even though Monod was writing after the fashion for cybernetics had begun to wane, he still argued that organisms were quite literally cybernetic structures, consisting of patterns of control and feedback that were embodied by the action of specific molecules. He also argued that the components of many cellular molecular networks interact in a way that is based on information, not on chemical structure. For example, when enzymes are induced by the presence of their substrate, this does not occur through a direct chemical link but through the activity of an intermediary protein and the gene that codes for it. The only way of understanding this process is as a flow of information that passes through each component, taking different physical forms as it goes.62

  Modern analytical techniques enable scientists to understand such biochemical interactions in exquisite detail. This has given rise to the field known as systems biology, which studies patterns of chemical interaction and gene regulation. Some claim that systems biology will embrace all the levels of life, right up to the ecosystem.63 For the moment, research under the systems biology label focuses on the chemical processes taking place in the cell. The vast data sets produced by such analyses, and the ability of modern computers to process and model those data, have inevitably led to a resurgence of interest in cybernetic approaches to biochemical processes, with a focus on the importance of feedback.64 However, despite the confidence of both Jacob and Monod that cybernetics would provide a way of understanding how the genetic code turns into instructions, the influence of cybernetics on modern science remains at the level of broad effects rather than any precise detail. That is even true in the field of neurobiology: although neural networks are clearly processing information and control, for most students and scientists cybernetics is a dimly remembered ancestor, rather than an essential part of their experimental approach.65

  Science, like other parts of human culture, can be influenced by fashion, and by our apparently endless appetite for novelty. When fashions change in science, it is not simply because people become bored and crave change, but because the old approach or technique has at best proved disappointing, at worst a failure. The influence of cybernetics and information theory on genetics can be seen in this way. In the 1940s and 1950s these two related approaches had a massive impact on the development of biology as a whole and on molecular genetics in particular. In the end, their influence waned as they failed to provide a framework that could stimulate further discovery. Both views ended up influencing genetics as vital metaphors and ways of viewing the world, not as essential theoretical foundations. This metaphorical role remains today, and it explains why scientists are so comfortable in saying that genes contain information and that they exert control over cellular networks.

  * These first reactions may not have occurred near deep-sea vents, but instead in small vesicles made of fatty acids. This is the view of Jack Szostak, who has been able to create such an artificial protocell and get RNA to replicate spontaneously within it (Adamala and Szostak, 2013).

  CONCLUSION

  In his book Ways of Knowing, the historian John Pickstone pointed out something that might seem obvious: science is a form of work. He argued that changes in how scientists gain their knowledge of the world can be interpreted in terms of changes in the organisation of work that have also occurred in manufacturing, which in different phases has been dominated in turn by what he called craft, rationalised production and systematic invention.1 The race to crack the genetic code was mostly a matter of craft. Individuals or small groups were struggling with ideas and concepts as much as they were with facts; they were not only trying to understand what would be the right experiment to answer a question, they had to work out what the question was. Only in its final phases, after the breakthrough of Nirenberg and Matthaei, did craft partially cede pride of place to something like rationalised production, as the answer became visible and knowable, although it had not yet been attained. During those years from 1961 to 1967, cracking the code gradually became as much about biochemical technology as it was about imagination, even if the development and application of that technology required a great deal of craft and insight.

  These discoveries created a revolution in our understanding and in our ways of thinking about life, a revolution that changed how science is done, shaping both our present and our future. In many respects we are now in a phase of systematic invention, in which new discoveries are being made in a more coordinated way, often involving large teams. Through the development of technology, we are now able to sequence the genomes of whole organisms in a matter of weeks – and soon even more quickly than that. Norbert Wiener, the founder of cybernetics, was concerned about how automation would alter factory work. It has most certainly transformed how science is done: robots can now decipher our genes, turning our genetic code into digital data that can then be explored anywhere in the world.

  In 1991, just as the genome projects were being dreamt up, Wally Gilbert published an article in Nature in which he looked to the future.2 Quite remarkably, he pretty much described the world we live in, suggesting that computers around the world would be hooked into databases, and that biologists would need to learn computing techniques to cope with the tide of data, investigating gene function first through a comparison of genes in different species rather than in an experiment. Gilbert pointed to skills that had already been lost in the brief history of molecular genetics, such as the ability to isolate restriction enzymes in the lab, which had been rendered obsolete by the availability of commercial products, and he rightly predicted that this process would continue. He also recognised that this rolling change was nothing new – once upon a time scientists blew their own glassware; later they bought it from a catalogue. The advent of automated sequencing of whole genomes is a huge step forwards – few scientists who went through the drudgery of hand-sequencing genes would want to return to those days. Scientists can now think about the biology instead of struggling with the chemistry.

  Who those scientists are and how they work together have also changed dramatically. The work that resulted in the cracking of the genetic code was virtually entirely carried out by men, with a few exceptions – in chronological order, the women featured here were Harriet Ephrussi-Taylor, Martha Chase, Rosalind Franklin, Marianne Grunberg-Manago, Maxine Singer, Leslie Barnett and Norma Heaton. Some of these women were leading scientists, others were mid-level researchers, still others were technicians. Women now have a far more significant role: most fields of biology include leading female scientists, and it is quite usual for women to run laboratories. Nevertheless, although there are generally more women studying biology at university, at PhD level this becomes approximately equal numbers of male and female students, and there is then a growing proportion of men as you go up the academic scale, culminating in an overwhelmingly male professoriate. We are still far from equality between the sexes.

  Most of the scientists described here were from the US, the UK and France. Science is now a truly international activity; even if the main contributors to the pages of the leading journals are still based in the richest and most developed countries, those researchers are often from all around the planet, with an increasing number coming from China. The current route for training a scientist involves not only a PhD but also a period of several years working in different laboratories, preferably in other countries, gaining experience and techniques. Most leading laboratories are now mini-United Nations. However, it is a striking fact that, even in the US, men and women of Afro-Caribbean origin are still substantially under-represented. In each country, the recruitment into science is biased by the multiple effects of race and class on educational attainment and on what is seen as being possible. Increasing the number of scientists from ethnic minorities and from th
e working class is a complex issue that science cannot solve on its own, but it needs to be addressed – at the moment there is a substantial pool of talent that we are not accessing because of inherent inequalities in our education system.

  The way in which these multinational teams work has also altered in comparison with the 1950s and 1960s. Although papers in genetics are still published by small groups and, very occasionally, by single individuals, there is a clear tendency for research to be produced by large multidisciplinary teams. This is especially the case in genomics, in which many groups from around the world may be involved in obtaining and analysing the data. In 2014, a paper appeared in Nature Genetics describing how hundreds of variants in the human genome contribute to differences in height between individuals; the article was signed by more than 440 authors.3 Big Science, typical of particle physics and astronomy, had not been seen in biology until the major genome sequencing projects. It is now becoming commonplace, changing the relationship of individual scientists to the work they produce, rendering each person’s contribution relatively minor and highly specific. The increasingly tight budgets of funding organisations encourage large teams by promoting multidisciplinarity and often require the probable outcomes to be clear before the experiments have begun. It seems unlikely that the small, curiosity-driven teams that led to the cracking of the genetic code would survive in today’s climate.

  How we think about genes and what they do has also been transformed. In the 1830s, when the word heredity was first applied to biological characteristics, they were said to be ‘passed down’, just like more worldly inheritances such as money, land or furniture. Once the electronic age began, characteristics were said to be transmitted; after the growth in interest in codes and computing during and following the Second World War, it seemed obvious to suggest that genes contain a code and transmit information. The most powerful metaphors in science are often those that flow from new technological developments. The summit of the current phase of technology is the computer – this is also the richest metaphor that science currently employs. Not all the metaphors we use to describe the genetic code and the way it functions are so complex – ‘transcription’ and ‘translation’ suggest that the code is a language that is written down, and is either copied from DNA into RNA (‘transcription’), or is turned into another language completely, that of proteins (‘translation’). These metaphors weigh heavily on how we think about the nature of the genetic code and what it does. The complex linguistic and computational metaphors wrapped up in the seemingly simple idea of a genetic code frame our ideas about heredity.

  But a ‘frame’ means two things – it both enables and limits how we think. We understand the nature of heredity with a far greater richness than people a century ago because of the wealth of research that has been done and also, because of the way in which we think about this research, the context in which we interpret it. But we are unable to conceive of other ways of viewing these phenomena because we do not yet have the appropriate metaphors. The frame is also a cage.

  Nirenberg and Matthaei, the first to crack the code, were outsiders, unaware of the debates of the previous decade that had led theoreticians to think that a repetitive sequence of bases would be meaningless. Their imaginations were free of the shutters that seem to have operated in thinking at other laboratories around the world. Ideas can help scientists understand data and can also prevent them from seeing what is under their nose: either way, they are essential to how science works.

  Metaphors and analogies carry a risk. It is easy to forget that a particular term is a figure of speech, a way of viewing a given phenomenon, rather than being literally true. A gene is like a computer program, but it is not a program and does not function according to the same rules, even though it may be usefully understood in this way. Organisms are not machines, even if they work on physical principles and share some features with devices we have invented. The genetic code is not literally a code and it is not a language. It is a process that enables organisms to carry out particular functions by turning stored information into structures or actions, using evolved systems of control.

  As became clear after the failed attempts to apply the strict mathematical view of information to genetic data, our way of describing information in genetics is primarily metaphorical. Although experimentation is generally the most powerful way of obtaining evidence that can test a hypothesis, to interpret this evidence we need theories and conceptual frameworks, which in turn are made up of words, metaphors and analogies. Understanding the power and limits of such metaphors will help us prepare for the breakthroughs of tomorrow, when we will reinterpret what we know and discover what we have yet to imagine.

  New technological and scientific developments will provide us with new metaphors, new ways of understanding how life works, and new approaches to manipulating molecules. That future will inevitably contain opportunities and challenges. Synthetic life may enable us to resolve major economic and ecological problems, or it may inadvertently threaten the human race and the ecosystem. We may find ourselves able to manipulate aspects of our behaviour or anatomy by deliberately and precisely changing our genes and those of our offspring. This might open the road to health, fulfilment and pleasure, but it will also pose major ethical dilemmas. By revealing and cracking the genetic code, science has shown itself capable of revealing life’s greatest secret. But science cannot tell us what to do with that secret, nor ensure that the knowledge and technology that flow from it are used for the greatest good of the many and with the least damage to the planet. Such a positive outcome will require the active involvement of the populations of all countries, as well as a clear understanding of the scientific and political issues raised by the amazing discoveries we have made and by the yet more amazing discoveries that are to come.

  UGA

  GLOSSARY AND ACRONYMS

  Amino acid. A small molecule containing amine (-NH2) and carboxylic acid (-COOH) groups. There are hundreds of different amino acids, but only twenty of them generally occur in organisms. They are strung together to make proteins.

  Anticodon. A sequence of three bases of RNA found on the small tRNA molecule, which bind with a codon on the mRNA molecule.

  AUG. The opening ‘word’ of a gene, this mRNA codon instructs the cell’s protein synthesis machinery to ‘start here’, thereby also setting the reading frame for the gene. When AUG occurs in the middle of a gene, it codes for methionine.

  Base. A molecule – adensoine, cytosine, guanine, thymine or uracil – that forms part of a nucleotide in DNA or RNA.

  Chromosome. Cellular structures composed of DNA and proteins that contain genes.

  Codon. A sequence of three bases in a DNA or RNA molecule that codes for an amino acid.

  CRISPR. A new technique for editing genes in organisms, using a method derived from bacteria. The name comes from the kind of sequences where the phenomenon was first observed: Clustered Regularly Interspaced Short Palindromic Repeats. The technique has enormous scientific and medical potential.

  Crystallography. The study of the molecular structure of crystals.

  Cybernetics. The study of control and information flow in organic, mechanical or electronic systems, with an emphasis on the ability of negative feedback to produce apparently purposeful behaviour.

  DNA. Deoxyribonucleic acid, a double helical molecule composed of a sugar/phosphate backbone and four bases: adenine, cytosine, guanine and thymine (A, C, G and T). The genetic material in all organisms and some viruses.

  Enzyme. A large biological molecule – made of either protein or RNA – that catalyses (speeds up) a particular chemical reaction. Essential for life to exist.

  mRNA. Messenger RNA. These molecules are copied from the gene and move from the chromosome to the ribosome, where they bind with a series of transfer RNA molecules, each of which is attached to an amino acid.

  Nucleic acid. RNA or DNA.

  Nucleoproteins. The mixture of proteins and nucleic acids th
at make up chromosomes.

  Nucleotide. A molecule that combines a base with a five-carbon sugar (ribose or deoxyribose) plus phosphate; forms the basis of the nucleic acid sequence.

  Operon. A group of genes that act under the concerted control of a single genetic element.

  PCR. Polymerase chain reaction. Technique developed in the 1980s for amplifying small sequences of identified DNA. Now routinely used in science, in medicine and in the legal system.

  Phage. Short for bacteriophage. These are viruses that attack bacteria.

  Protein. A large molecule consisting of chains of amino acids. Proteins come in a vast variety of forms and carry out many biological functions.

  Purines. Ring-shaped molecules, rich in nitrogen, larger than pyrimidines. In DNA and RNA, adenine and guanine are purine bases; each pairs with a particular pyrimidine (A with C, G with T or U).

  Pyrimidines. Ring-shaped molecules, rich in nitrogen, smaller than purines. In DNA, cytosine and thymine are pyrimidine bases; in RNA, thymine is replaced by uracil. Each pairs with a particular purine (C with A, T or U with G).

  Reading frame. In a DNA or RNA sequence, the correct order in which the bases should be read.

  Repression. Inhibition of gene function.

  Ribosome. Complex RNA structure found in all cells that is the primary site of protein synthesis.

  RNA. Ribonucleic acid. A helical molecule composed of a sugar/phosphate backbone and four bases: adenine, cytosine, guanine and uracil (A, C, G and U). The genetic material in some viruses; carries out a wide range of regulatory functions in all cells.

  Specificity. A term widely used until the 1960s to describe the various qualities of molecules and in particular the ability of proteins to carry out many functions.

  Transcription. Copying of the genetic message from DNA to RNA.