Simulating Evolutionary Processes of Stochastic Genetic Networks in Volatile Environments

Abstract

This work presents a simulator for modeling evolving cell populations. The gene network dynamics are simulated using a delayed stochastic simulation algorithm at single event and single molecule level. Moreover, modeling strategies of such complex systems are discussed. The simulator can be used to implement genetic circuits using typical genetic operators such as reproduction, mutations, and exchange and deletion of genetic material, in arbitrary fashion, and the evolving populations can be modeled in transient stochastic environments, enabling studies of the pathways of evolution in such unpredictable conditions. To demonstrate its applicability, two biologically relevant examples are presented. In the first example, the effects of environmental changes to the phenotypic diversity and mutation rates are studied. Moreover, it is shown that evolution can generate complex distributions of phenotypes, depending on the nature of the mutations. Using the second example, it is shown that small changes in the evolutionary constraints can drive a population to favor different levels of stochasticity in their cellular processes, and how small changes in the details of these processes will lead to generation of phenotypes with significant evolutionary advantage. /Kir11