Advances in the stochastic and deterministic analysis of multistable biochemical networks
Creators: name: Petrides, Andreas
Publisher: name: Apollo - University of Cambridge Repository
Year: 2018
Subjects: subject: Markov processes
subject: Chemicals
subject: Mathematical model
subject: Switches
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Steady-state
subject: Stochastic systems
subject: bimodal stationary distribution
subject: low feedback gain
subject: steady state distribution
subject: discrete genetic toggle switch phenomena
subject: Markov chain tree theorem
subject: low dimensional nonlinear deterministic systems
subject: discrete state stochastic systems
subject: biological systems
subject: graphical discrete nullcline-like construction
subject: original genetic toggle switch
subject: feedback interconnection
subject: mutually repressing genes
subject: Pratt Tableau
subject: Multistability
subject: Root locus
subject: CaMKII

description: 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 $Ca^{2+}$/calmodulin-dependent protein kinase II (CaMKII), a key enzyme in synaptic plasticity.
description: EPSRC (1468514)