Oral Presentation Society for Molecular Biology and Evolution Conference 2016

Stochastically varying environments promote evolution of modularity and hierarchy in simulated bacterial metabolic networks (#235)

Aaron Goodman 1 , Marcus Feldman 1
  1. Stanford University, Stanford, CA, United States

A typical E. coli bacterium undergoes a complete cell cycle every 40 minutes, in doing so it hydrolyzes 10 to 50 * 109 ATP molecules. The process is carried out by around 500 metabolic enzymes and around 1200 distinct metabolites. A bacterium's ability to reproduce depends on the efficiency of its metabolism. The complex metabolisms of bacteria are often studied as a network of metabolites linked together by the enzymes that transform one metabolite into another. The properties of these networks vary across the bacterial kingdom and are influenced by the bacterial life histories. It has been found that bacteria evolve modular networks to survive in changing environments, and evolve hierarchical networks to optimally process metabolites when the environment is stable.

However, despite apparent opposing selective pressures for hierarchy in stable environments, and modularity in varying environments, degree of hierarchy and modularity are correlated in real world metabolic networks. We use an artificial chemistry approach to simulate the evolution of metabolic networks to show how evolution in varying environments can affect the topological properties of bacterial metabolic networks.

Using a simplified model of bacterial metabolisms in which the number of enzymes and metabolites is significantly restricted, we are able to simulate evolution of organisms and reconstruct metabolic networks. We find that the way in which environments vary, whether predictably, or randomly greatly impacts the topology of the optimal metabolic networks. In random unpredictable environments, hierarchy is advantageous and modularity disadvantageous. However artificial metabolic networks that evolve in stochastically varying environments have the same topological properties as real world bacterial metabolic networks.

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