Abstract

Recent phylogenetic studies reveal that Horizontal Gene Transfer (HGT) events are likely ubiquitous in the Tree of Life. However, our knowledge of HGT's role in evolution and biological organization is very limited, mainly due to the difficulty tracing HGT events experimentally, and lack of computational models that can capture its dynamics. Here, we present a novel, multi-scale model of microbial populations with the capacity to study the effect of HGT on complex traits and regulatory network evolution. We describe a parallel load-balancing framework, which was developed to overcome the innate challenges of simulating evolving populations of such magnitude and complexity. Supercomputer simulations of in silico cells that mutate, compete, and evolve, show that HGT can significantly accelerate, but also disrupt, the emergence of advantageous traits in microbial populations. We show that HGT leaves a lasting imprint to gene regulatory networks when it comes to their size and sparsity. In any given experiment, we observed phenotypic variability that can be explained by individual gain and loss of function during evolution. Analysis of the fossil mutational and HGT event record, both for evolved and non-evolved populations, reveals that the distribution of fitness effect for HGT has different characteristics in terms of symmetry, shape and bias from its mutational counterpart. Interestingly, we observed that evolution can be accelerated when populations are exposed in correlated environments of increased complexity, especially in the presence of HGT.

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