Probabilistic modelling of noise as a driving force in biological systems

Systems biology takes a mechanistic, relational approach to the study of biological processes, commonly finding expression in mathematical models. Hypotheses about systems can be tested when formulated as models, and promising avenues for further study identified. A model sufficiently faithful to the system under study can be used to guide experiments, to probe the system in silico, and to learn about emergent features not evident from the static picture of the system. In this work, three contributions to the modelling community are proffered. First, a computational package is presented that implements an algorithm for the validation and parametrisation of a model. In validation, we are asking how likely we were to make some observation, given the model, or, equivalently, how able the model is to explain the data. The subsequent two contributions concern noise in biological systems. Biological systems display inherent variability, or noise, due to the stochastic mechanisms through which biochemical processes occur. This variability can be critical to the behaviour of a system and to the fates of individual cells. With this in mind, the second contribution is the development of a methodology to model protein-dependent population dynamics. The idea is to model cell population dynamics that result of noisy intracellular protein dynamics. The method's application is demonstrated in population-level models of a protein-dependent cell cycle and yeast antibiotic resistance. Given an appreciation of the pivotal effects of noise, the third and final contribution is a study of the mechanism of noise propagation. I present an analysis of the contributions of biochemical reaction motifs to the creation and transmission of noise that ultimately manifest in observations of biological systems. This study points to specific processes that enhance or attenuate noise, with the aim of beginning to unravel the flow of noise through a system.Open Acces