Extending the evolvability model to the prokaryotic world: Simulations and results on real data

In 2006, Valiant introduced a variation to his celebrated PAC (Probably Approximately Correct) model to biology, by which he wished to explain how, with two simple mechanisms - random variation and natural selection - complex life mechanisms evolved in such a short time. Subsequently, several works extended and specialized the evolvability framework to more specific processes. In this study, we extend the evolvability framework to accommodate horizontal gene transfer, the transfer of genetic material between unrelated organisms. While in a separate work, we focused on the theoretical aspects of this extension and its learnability power; here, the focus is on more practical and biological facets of this new model. Specifically, we focus on the evolutionary process of developing a trait and model it as the conjunction function. We demonstrate the speedup in learning time for a variant of conjunction to which learning algorithms are known. We also confront the new model with the recombination model on real data of Escherichia coli strains under the task of developing pathogenicity and obtain results adhering to current existing knowledge. Apart from the sheer extension to the understudied prokaryotic world, our work offers comparisons of three different models of evolution under the same conditions, which we believe is unique and of a separate interest.