Evolving catalytic reaction sets using genetic algorithms

We construct simple artificial chemistries in order to gain an understanding of how a chemical reaction network might emerge from a state of relative disorder in non-living “protocells”. Such chemistries have relevance to origin-of-life studies as well as to artificial life research. We present a model comprised of interacting polymers, and specify two initial conditions: a distribution of relatively disordered polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is then used to find a set of reactions that exhibit a pre-specified behavior. Our results show that reaction sets can be found to give polymer distributions that are biased towards longer polymers. We present examples of these protocell chemistries and show that the reaction sets found are robust in the sense that they produce desirable behavior in equilibrium. Such a technique is useful because it allows an investigator to determine whether a specific distribution can be produced, and if it can, a reaction network can be found and then analyzed