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Integrating In-Silico Models with In-Vitro Data to Generate Novel Insights into Biological Systems

Conway, D (2019) Integrating In-Silico Models with In-Vitro Data to Generate Novel Insights into Biological Systems. Doctoral thesis, Liverpool John Moores University.

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Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Systems Biology; in-silico; mathematical modelling; cell migration; Nrf2; NF-kappaB; Petri-net
Subjects: Q Science > QA Mathematics
Divisions: Applied Mathematics (merged with Comp Sci 10 Aug 20)
Date Deposited: 14 Feb 2019 10:54
Last Modified: 21 Dec 2022 11:51
DOI or ID number: 10.24377/LJMU.t.00010163
Supervisors: Webb, S
URI: https://researchonline.ljmu.ac.uk/id/eprint/10163
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