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Cell states, fates and reprogramming: insights from neural networks, graphical and computational approaches

Student thesis: Doctoral ThesisDoctor of Philosophy

Lineage specification was long thought to be an irreversible developmental process. However, with the advent of cell reprogramming and the discovery of induced pluripotent stem cells (iPSCs), it was shown that differentiation is in fact reversible. Cell reprogramming has mainly been studied experimentally, with no universally accepted theory explaining the phenomena. The purpose of this thesis is to drive forward our understanding of cell biology, by introducing analytical models for the interaction between genes and studying the transitions between the emergent cell types. This is done by appealing to key concepts from biology and employing tools commonly used in the field of statistical physics. Inspired by models of neural networks, a model for cell reprogramming is introduced in which cell types are hierarchically related dynamical attractors corresponding to cell cycles. Stages of the cell cycle are fully characterised by the configuration of gene expression levels, and reprogramming corresponds to triggering transitions between such configurations. Two mechanisms were found for reprogramming in a two-level potency hierarchy: cycle specific perturbations and a noise-induced switching. The former corresponds to a directed perturbation that induces a transition into a cycle-state of a different cell type in the potency hierarchy (mainly a stem cell) whilst the latter is a priori undirected and could be induced, e.g. by a (stochastic) change in the cellular environment. The reprogramming model is governed by the interaction between gene expression levels, as originally hypothesised by Waddington in his Epigenetic Landscape analogy. To further develop the biological significance, a detailed mechanism for these interactions between genes, in the form of regulation through transcription factors, is studied. This consists of constructing a bipartite graph framework for gene regulatory networks. A technique that integrates the genome and transcriptome into a single regulatory network. With this perspective, we are able to deduce important features of the regulatory network that exists in every cell type, such as the typical interactions required to sustain a net gene expression profile and how regulatory interactions must change to support multicellular life.
Original languageEnglish
Awarding Institution
Award date1 Jun 2019


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