Dynamics of Biomedical Networks

  • Simon E Godwin

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

Abstract

Biological systems are complicated, formed of many interacting parts. Network science is well placed to attempt to elucidate these systems, and it is important to further our understanding to look at different biological networks across scales and applications. For this thesis, we examined two biological systems that can be described with networks: gene regulatory networks (GRN) and functional brain networks. We developed and analysed a novel GRN describing pluripotent mouse embryonic stem cells (mESCs) dynamics. Pluripotency is controlled by a complex GRN; understanding the interplay between the network elements and factors present in the different media cultures could help define optimal culture protocols. We expanded an existing GRN describing Nanog (a master regulator of pluripotency) dynamics to include additional genes and both chemicals present in ground-state pluripotency media, i.e. 2i+LIF. We showed, using stochastic differential equations and bifurcation analysis, that the experimental dynamics of Nanog result from the combination of feedback loops in the GRN and transcriptional noise, giving rise to bistability. The functional brain network analysis we performed was in exploring Multiple Sclerosis (MS), a demyelinating disease of the central nervous system. This damage does not correlate with impairment, suggesting the brain can undergo neuroplasticity to compensate. We used functional magnetic resonance imaging (a technique to indirectly measure activity in the brain) collected in Bristol (CRICBristol) to explore neuroplasticity. We preprocessed the scans, extracted time series from regions of interest (ROI) and measured the functional connectivity (FC) between each ROI time series. We abstracted the brain to a network, where nodes are different regions of the brain and edges represent the FC between them. We measured different properties of the networks and compared them between MS and healthy controls (HC). We found no evidence of neuroplasticity between MS and HC, which could be because the MS cohort brains have already successfully undergone plasticity to compensate for the damage caused. Future work for the GRN network could be in using it to understand how chemicals known to interact with its elements could be applied to control pluripotent behaviour of mESCs. For the functional brain networks, a dynamic causal modelling (DCM) analysis could be done to elucidate if there are any differences in FC between MS and HC.
Date of Award28 Nov 2019
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorLucia Marucci (Supervisor) & Naoki Masuda (Supervisor)

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