The potential power of dynamics in epistasis analysis

Abstract

Choose an item., Choose an item.ii ACKNOWLEDGMENTS Thank you for investing your time in reading my thesis. I would like to thank my supervisor, Theordore J. Perkins, for his assistance, supervision and valuable feedback throughout the duration of this research project. I would like to thank my co-supervisor, Marcel Turcotte, our collaborating team and members of our lab. I would also like to thank my parents for their ongoing encouragement and support.iii ABSTRACTInferring regulatory relationships between genes, including the direction and the nature of influence between them, is the foremost problem in the field of genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. More specifically, a "surprising" situation occurs when the phenotype of a double mutant has a similar, aggravating or alleviating effect compared to the phenotype resulting from the single deletion of either one of the genes. As useful as this broad approach has been, there are limits to its ability to discriminate alternative pathway structures, meaning it is not always possible to infer the relationship between the genes. Here, we explore the possibility of dynamic epistasis analysis. In addition to performing genetic perturbations, we drive a genetic pathway with a dynamic, time-varying upstream signal, where the phenotypic consequence is measured at each time step. We explore the theoretical power of dynamic epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We also explore the identifiability of individual links in the pathway. Through these evaluations, we quantify how helpful the addition of dynamics is. We believe that a dynamic input in addition to epistasis analysis is a powerful tool to discriminate between different networks. Our primary findings show that the use of a dynamic input signal alone, without genetic perturbations, appears to be very weak in comparison with the more traditional genetic approaches based on the deletion of genes. However, the combination of dynamical input with genetic perturbations is far more powerful than the classical epistasis analysis approach. In all cases, we find that even relatively simple input dynamics with gene deletions greatly increases the power of epistasis analysis to discriminate alternative network structures and to confidently identify individual links in a network. Our positive results show the potential value of dynamics in epistasis analysis.