Advances in the stochastic and deterministic analysis of multistable biochemical networks

Change log
Petrides, Andreas 

This dissertation is concerned with the potential multistability of protein concentrations in the cell that can arise in biochemical networks. That is, situations where one, or a family of, proteins may sit at one of two or more different steady state concentrations in otherwise identical cells, and in spite of them being in the same environment. Models of multisite protein phosphorylation have shown that this mechanism is able to exhibit unlimited multistability. Nevertheless, these models have not considered enzyme docking, the binding of the enzymes to one or more substrate docking sites, which are separate from the motif that is chemically modified. Enzyme docking is, however, increasingly being recognised as a method to achieve specificity in protein phosphorylation and dephosphorylation cycles. Most models in the literature for these systems are deterministic i.e. based on Ordinary Differential Equations, despite the fact that these are accurate only in the limit of large molecule numbers. For small molecule numbers, a discrete probabilistic, stochastic, approach is more suitable. However, when compared to the tools available in the deterministic framework, the tools available for stochastic analysis offer inadequate visualisation and intuition. We firstly try to bridge that gap, by developing three tools: a) a discrete `nullclines' construct applicable to stochastic systems - an analogue to the ODE nullcines, b) a stochastic tool based on a Weakly Chained Diagonally Dominant M-matrix formulation of the Chemical Master Equation and c) an algorithm that is able to construct non-reversible Markov chains with desired stationary probability distributions. We subsequently prove that, for multisite protein phosphorylation and similar models, in the deterministic domain, enzyme docking and the consequent substrate enzyme-sequestration must inevitably limit the extent of multistability, ultimately to one steady state. In contrast, bimodality can be obtained in the stochastic domain even in situations where bistability is not possible for large molecule numbers. We finally extend our results to cases where we have an autophosphorylating kinase, as for example is the case with Ca2+/calmodulin-dependent protein kinase II (CaMKII), a key enzyme in synaptic plasticity.

Vinnicombe, Glenn
Markov processes, Chemicals, Mathematical model, Switches, Genetics, Steady-state, Stochastic systems, bimodal stationary distribution, low feedback gain, steady state distribution, discrete genetic toggle switch phenomena, Markov chain tree theorem, low dimensional nonlinear deterministic systems, discrete state stochastic systems, biological systems, graphical discrete nullcline-like construction, original genetic toggle switch, feedback interconnection, mutually repressing genes, Pratt Tableau, Multistability, Root locus, CaMKII
Doctor of Philosophy (PhD)
Awarding Institution
University of Cambridge
EPSRC (1468514)