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**Has your research helped you to reach any definition of life?**

Yes, there is a definition of life implicit in this model. Let me say, by the way, this model does not deal at all with the origin of life, with the creation of life. Basically, in the model I have you start off with life present immediately. And what you study is how it evolves, how it increases in complexity. You study biological creativity. That’s because in my model I’m already assuming, essentially, DNA, because when I assume a universal programming language I’m essentially assuming DNA and all the related mechanisms are there already. So I already have life: what happens is life gets more sophisticated, it evolves. Now, let me contrast that a little bit with population genetics. Dawkins, in *The selfish gene*, calls Ronald Fisher “the greatest biologist since Darwin”, and he says that because Fisher has a mathematical theory of evolution. And this theory, called Population genetics -Fisher, Wright, Haldane, many people worked on it- studies how gene frequencies change in response to selective pressures, and it’s a very nice theory, I have nothing against it, it’s a beautiful theory, but in this theory you start off with a fixed gene pool, and what you study is how frequencies change. So you don’t study where new genes come from. And that’s what interests me, biological creativity. Also, from a mathematical point of view, population genetics is very beautiful and you’re using very well-known mathematics, differential equations, whereas I’m using a very new kind of mathematics, starting off with Turing in 1936, computability theory and, in particular, algorithmic information theory. So this is newer kind of mathematics, and basically what I’m working on is the idea that DNA is essentially a digital programming language, which is a metaphor that is mentioned a lot: evo-devo, evolutionary developmental biology, talks about DNA as a programming language that calculates the organism, that specifies how to create the organism through embryo development. So that’s a very well-known metaphor and I guess what I’m adding to it is saying: “we have an opportunity here to try to create mathematical models and prove things.” Now, most of the current work, that I’m aware of, in modeling biology, is very different from what I’m doing. Systems biology, which is the fashionable thing to do now, tries to have computer models of biological systems which are very detailed and very realistic. And this is very useful: for example, the hope would be to test the effect of a drug, via computer simulations, rather than on living organisms. This is a very hot subject a lot of people are working on. But I think it’s hopeless to prove theorems with systems biology because you have very complicated, detailed models, which is good for doing a simulation, but is not good for proving things. This is sort of an epistemological issue of how pure mathematics works. I could give you some amusing quotes to make this point, that theoretical physicists are well aware of: that toy models are useful to understand physical systems, because the real systems are too complicated. One way to make this point is to quote Picasso: he made a remark, the English version of which goes something like “Art is a lie that helps us to see the truth”. And I modified it slightly: “theories are lies that help us to see the truth.” I can also quote John Maynard Smith, a theoretical biologist, and Jacob Schwartz, a mathematician. I had the good fortune to meet John Maynard Smith, by the way, before he died, and Jacob Schwartz was a friend of mine -he’s also dead, unfortunately. And they both have very quotable remarks. One of them, by Maynard Smith in his book *The origins of life*, says it’s a mistake to think that very complicated models are useful in biology: very complicated models only have the effect of confusing you. You need to work with simple models, otherwise it’s hopeless to try to understand their behavior. I can give you another quotation, from an essay by Jacob Schwartz, called *The pernicious influence of mathematics on science*: in there, he says that pure mathematics is not good at dealing with real situations, which are normally complicated and have several things going on at the same time. Pure mathematics works best when you’re studying a single phenomenon. Pure mathematics is, to put it some way, single-minded, and it works best when you take a simple idea and elaborate its consequences, but does not work very well in a more realistic situation when more than one thing is going on at the same time. Then pure mathematics tends to get lost in a jungle of combinatorial complexities. You can find these two quotations in the lecture notes on metabiology I have posted in my website, at the very beginning.

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