Network Thinking Applied to Modeling of Biological Evolution: From Gene Regulatory Circuits to the Transition Space of Genotypes

Theories of evolution illuminate why an organism is the way it is and generate testable hypotheses for empirical research across the biological sciences. With accumulated molecular studies, it is key to speculate theory enlightened by the connection between an individual's hereditary information (genotype) and its observable traits (phenotype). In particular, both our theoretical and empirical knowledge of genotype-to-phenotype maps is rapidly advancing, and our perception of their impacts on evolutionary phenomena is far from complete. In this thesis, I will explore how encapsulating genotype-phenotype mapping through network science broadens our theoretical understanding of evolution. Specifically, I focus on the evolutionary dynamics of gene regulatory networks and develop a modeling framework that renders both interpretability and potentially novel insights for evolutionary processes. From computational analyses, the modeling framework elucidates why and how reproductive barriers rapidly emerge between geologically separated, i.e., allopatric, populations even without divergent selection forces. Furthermore, I analytically show that the equilibrium probability distribution of regulatory circuits is directly predicted by topological properties of the mutational network connecting individual viable regulatory circuits. This ``network of networks" characterizes the space of possible genotypic transitions, and it maps population genetics models to dynamical processes on graphs. Finally, I will provide algorithmic solutions for the evolutionary dynamics of gene regulatory networks in more complex biological scenarios using message passing techniques. Combined, my proposed research explores theoretical illumination on evolutionary phenomena that one can learn from the connection between genotypes and phenotypes, as well as how evolution shapes our prior belief of the genotype-phenotype maps.

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December 13, 2019
9:45 am
9:45 am
Network Science Institute
177 Huntington Ave