Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770432
Title: Spatial stochastic modelling of biological processes
Author: Jones, Anna Christine
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2018
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Abstract:
Computational simulation of cellular processes can provide us with important insights into the dynamics of a cell. In particular, stochastic simulation has proven to be an invaluable tool for modelling sub-cellular environments, due to the intrinsic noise of not knowing when reactions will occur in a cell and which molecules will react. This uncertainty is amplified when there are limited numbers of molecules within a cell, as the probability of reactions occurring becomes small. One aspect of cellular dynamics that is often overlooked in stochastic models is the effect of spatial constraints on the behaviour of a cell. Cellular processes depend not only on the number of molecules present, but also on the distribution of these molecules within the cell and how they move and interact with each other. For this reason, we hypothesised that spatial stochastic simulations of cells would prove to be more effective at recreating experimentally observed cells than spatially-averaged models. To begin testing this hypothesis, we create and compare spatially-dependent models against spatially-averaged models for three biological processes. The first two processes we study involve post-transcriptional gene regulation by small RNAs. An overarching theme across each biological process that we have studied in this thesis is the implication in tumourigenesis. We present examples in which stochasticity and spatial detail detail can produce different modelling results when compared with non-spatial models. We also show that, in many cases, the biological systems we have chosen to model behave somewhat deterministically. We have shown that there are threshold values for certain parameters, for example, molecular concentrations, at which the systems largely follow the dynamics of the deterministic models. Gene expression is also inherently stochastic; low numbers of genes, proteins and mRNAs are found within many cells, and many genes are controlled by probabilistic interactions between these molecules. In particular, if some molecular species are scarce then the likelihood of these molecules finding a binding partner becomes probabilistic. Therefore, stochastic modelling is an appropriate technique for studying this biological process. In this thesis, we study cellular signalling processes as stochastic processes. Specifically, we built a stochastic model of the canonical Wnt/Beta-catenin signalling pathway and compared this with existing deterministic models. We present here some preliminary results and a proof-of-concept stochastic. An obvious path for further investigation would be to expand on this model by including cell-cell adhesion and movement of molecules between cellular compartments in a crowded environment.
Supervisor: Byrne, Helen ; Burrage, Kevin Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.770432  DOI: Not available
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