Under the microscope: Engineers are into genes too

Computer scientists are looking to Darwin for ideas
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The Independent Culture
THE RELATIONSHIP between engineering and biology has many unsuspected aspects. For example, people are somewhat surprised when they learn that I was trained as a civil engineer and am now a biologist who works on embryos. My switch was initiated by a friend who knew that I wanted to change careers and told me of work being done on measuring the mechanical properties of the cell membrane in order to try to understand how the cell splits into two at the end of cell division. My transition to research on this problem was made possible by the Nuffield Foundation, which at that time was offering scholarships for graduates in the physical sciences to encourage them to change to biology. In fact the biological sciences have benefited considerably from the entry of those trained in the physical sciences - two of my heroes are the molecular biologist Francis Crick, who was originally a physicist, and the evolutionary biologist John Maynard Smith, who was an aeronautical engineer. I have no doubt that my own work in biology was heavily influenced by my training in engineering. But times have changed and now it is the engineers who are looking to see what they can learn from biology.

I learned this at a recent meeting of electronic engineers, mathematicians and a sprinkling of biologists in Lausanne, where they were discussing computing using biological concepts. Two of the scientists at the meeting had written a paper entitled Embryonic Electronics. The abstract begins thus: "In recent years we are witness to a growing interest among engineers in nature's workings, giving birth to novel engineering methodologies inspired by biological processes. Our ultimate objective is the construction of large-scale integrated circuits, exhibiting the properties of self- repair (healing) and self-replication..." They have developed a silicon- based artificial cell that they hope will be able to generate the self- repair properties that will be necessary as electronic chips become ever more complex. Although the paper derives many of its ideas from my description of embryonic development in The Triumph of the Embryo (now out of print), I could, alas, follow neither their paper nor the talks. However a young researcher from Sussex University helped me understand at least one aspect of the current enthusiasm for genetic algorithms in computing.

Genetic algorithms are a technique derived from Darwin's ideas about natural selection and evolution. The idea is to use the computer to generate, randomly, a number of solutions to a particular problem that you want to solve. You then look to see which have been been best at providing a solution and you select these, the fittest, eliminating the others. You then let these fit solutions mate, as it were, exchange information, and so breed, and also introduce some mutations. Repeating this procedure again and again can give novel and valuable solutions.

Adrian Thompson has used this approach to design a microprocessor that could do a simple task like responding appropriately to the commands 'Go' and 'Stop'. Instead of designing the circuit in the standard digital way using the on-off switching computer technology, he wanted to exploit all the properties of silicon in the way that biological systems exploit, for example, the chemistry of proteins. The approach worked remarkably well and the processor could detect subtle difference between Go and Stop. But how did the processor actually work? Thompson had not designed it so much as let it evolve. The system had, for example, developed a clock but Thompson does not really understand how it works. It is so complicated because it uses all the resources of silicon, the physical properties of which he is unaware. This approach could be used to develop processors that work over a wide range of temperatures, for example, but more complex examples may not be accessible to human understanding. Evolution by selection is an astonishingly powerful process for generating complex functioning systems and may turn out to be as important in engineering as it has been in biology. Clearly, I need to learn some engineering again.

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