|Talks|

Network thinking applied to modeling biological evolution: From gene regulatory circuits to the transition space of genotypes

Dissertation defense
Past Talk
Chia-Hung Yang
Apr 15, 2021
12:00 pm
Apr 15, 2021
12:00 pm
In-person
4 Thomas More St
London E1W 1YW, UK
The Roux Institute
Room
100 Fore Street
Portland, ME 04101
Network Science Institute
2nd floor
Network Science Institute
11th floor
177 Huntington Ave
Boston, MA 02115
Network Science Institute
2nd floor
Room
58 St Katharine's Way
London E1W 1LP, UK

Talk recording

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.

Committee members:

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

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Apr 15, 2021