The Software of Life

Being alive, we tend to think that life is easy to grasp. In the accepted classification of sciences, mathematics is thought to be the queen, and the most difficult to grasp, followed by physics, chemistry, and, finally, biology. But this scientific hierarchy is false and misleading: we now know that biology contains more mathematics than we ever imagined.

When molecules entered the scientific understanding of life with the discovery of DNA, biology climbed one step up the scale, to chemistry. Then, with recognition of the abstract schemas dictating how genes are expressed, biology climbed even closer to mathematics.

Today’s buzzword in the study of life is “systems” biology. For a long time, those who studied the nature of life and heredity were divided into two camps: epigeneticists , who emphasized environmental influences on living organisms, and preformists , who stressed the similarities between parents and progeny. The epigeneticist view was clearly wrong, because something stable had to be transmitted across generations. But the preformist view that the entity transmitted across generations was the whole organism was contradicted by the impossibility of segmenting objects infinitely.

What had to be transmitted was not the final organism, but the recipe to make it. Consider the old metaphysical puzzle: is a wooden boat whose planks are gradually replaced as they decay the same boat after all the planks have been changed? “Systems” biology is biology that recognizes that what remains the same is the design of the boat – that which determines the relationships between the planks.

This thought paved the way for the concept of a “genetic program,” akin to a computer program – a metaphor that became almost self-evident when the structure of DNA was discovered, because DNA could be visualized as a linear string of symbols, which is exactly what computers read as a program. Like a computer program, DNA does not preserve the final state of what it codes for; rather, it embeds in a symbolic but concrete way (it is a real “text”) the relationships between all the objects and agents that it specifies and controls.

A remarkable observation supports this analogy: viruses behave like individual pieces of programs, using the cell as the machine needed to make them multiply and subsequently propagate (often by destroying the machine). When computer programming became widespread, pieces of software were found to behave the same way, and were thus called “viruses.” And when it became possible to manipulate DNA in vitro, the metaphor of a “genetic program” appeared even more precise: scientists could construct experiments that corresponded to the reprogramming of cells merely by working on symbols in silicon.

The metaphor comes from the famous mathematician and computer scientist Alan Turing, who, along with John von Neumann and other theoreticians, uncovered the link between the mathematics of whole numbers and logic. Turing proposed that all computations and logical operations could be performed by a simple machine, which he called the Universal Turing Machine, reading and modifying a linear sequence of symbols. This required only the physical separation of the symbols (visualized as a tape) handled by the machine and the machine itself. Moreover, the tape carried the data that allowed the machine to proceed. So the data could be split into two types: a program that embedded the “meaning” of the logical sequence recognized by the machine, and the pure data that provided the context for the program to run.

Genetic engineering rests on the manipulation of DNA molecules (whether real or artificially constructed) in order to reprogram foreign cells. As a result, many bacteria today produce human proteins. But this represents only a small part of the genetic program. Transfer of genes between organisms is widespread. Nuclear cloning, illustrated by the sheep Dolly, has made the Universal Turing Machine a highly revealing, if not all explaining, model of the cell.

If we take this metaphor at face value, there is a surprising consequence. It has been shown that, the outcome of some computer programs is at once entirely deterministic, innovative, and unpredictable. The computer metaphor thus implies that living organisms are material systems that, facing an unforeseeable future, arrive at improbable solutions so that some of their progeny can survive in unpredictable conditions. Life is inherently creative.

However, the metaphor is limited by a simple fact: computers do not make computers. The challenge for the new biology is to understand how they would.