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 dissertation, we explore how encapsulating genotype-phenotype mapping through network science broadens our theoretical understanding of evolution. Specifically, we focus on the evolutionary dynamics of gene regulatory networks (GRNs) 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, we 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, we study this network of networks as the underlying skeleton of a fitness landscape. Mapping genotypes to phenotypes through the gene regulatory networks theoretically reveals noteworthy topographical features of the fitness landscape, and we derive an optimal mesoscopic characterization and an efficient constructive algorithm for the modeled landscape. Combined, this dissertation 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.
Samuel Scarpino (Chair), Network Science Institute, Northeastern University
Alessandro Vespignani, Network Science Institute, Northeastern University
Katie Lotterhos, Department of Marine and Environmental Sciences, Northeastern University
Robin Hopkins, Department of Organismic and Evolutionary Biology, Harvard University
Chia-Hung is a fifth-year PhD Candidate working in the Emergent Epidemics Lab. He received his BA in Physics from National Tsing Hua University in Taiwan. He is interested in how the topology of networks advances our understanding of complex systems. Currently he works with Professor Samuel Scarpino on a study of how gene regulatory networks evolve, looking for a network-interpretation of traditional evolution theories.