Derivation and Use of Gene Network Models to Make Quantitative Predictions of Genetic Interaction Data

Abstract

This thesis investigates how pairwise combinatorial gene and stimulus perturbation experiments are conducted and interpreted. In particular, I investigate gene perturbation in the form of knockout, which can be achieved in a pairwise manner by SGA or CRISPR/Cas9 methods. In the present literature, I distinguish two approaches to interpretation: the calculation of stimulus and gene interactions, and the identification of equality among phenotypes measured for distinct perturbation conditions. I describe how each approach has been applied to derive hypotheses about gene regulatory networks. I identify conflicts and uncertainties in the assumptions allowing these derivations, and explore theoretically and experimentally approaches to improve the interpretation of genetic interaction data. I apply the approaches to a well-studied gene regulatory branch of the DNA damage checkpoint (DDC) pathway of Saccharomyces cerevisiae, and confirm the known order of genes within this pathway. I also describe observations that seem inconsistent with this pathway structure. I explore this inconsistency experimentally and discover that high concentrations of the DNA alkylating drug methyl methanesulfonate cause a cell division arrest program distinct from a G1 or G2/M checkpoint or from DNA damage adaptation, that resembles an endocycle.