Model-Checking Based Approaches to Parameter Estimation of Gene Regulatory Networks

The expression of genes is a fundamental process in living cells, both eukaryotic and prokaryotic. The regulation of gene expression is achieved via sophisticated networks of interactions between DNA, RNA, proteins, and small chemical compounds. The qualitative and quantitative characterisation of interactions between genes is one of the major current research targets in systems biology. In this PhD research project, we view gene regulatory networks as Markov chains, resulting from popular formalisation frameworks such as Dynamic Bayesian Networks and Probabilistic Boolean Networks. This will allow us to reason about both the structure and strength of gene interactions. Our goal is to develop new algorithms and tools, which are tailored for the modelling and analysis of gene regulatory networks, by exploring model checking techniques that have been developed and widely used in computer science. More specifically, we will combine model checking techniques with sampling and optimisation methods from the literature to derive new techniques to solve the parameter estimation problem of Markov models of gene regulatory networks.