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Modelling cellular gene expression via neural networks and bipartite graphs

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Cell differentiation is one of the most fascinating areas of developmental biology. This was long thought to be an irreversible process, however it has been shown recently that it is possible to reprogramme fully differentiated cells into a state of induced pluripotency, which strongly resembles embryonic stem cells, via the introduction of a few transcription factors. This opens up exciting perspectives in the field of regenerative medicine, however, no universally accepted theory exists that explains the phenomena. The purpose of this work is to drive forward our understanding of cell reprogramming by introducing an analytical model for transitions between cell types. Inspired by neural networks theory, we model cell types as hierarchically organized 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: cycle-state 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 (e.g. a stem cell) whilst the latter is a priori undirected and could be induced, e.g. by a (stochastic) change in the cellular environment. In addition, the mechanism for the effective interactions arising between genes, is studied by means of a bipartite graph model, 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.

This talk is part of the The Archimedeans (CU Mathematical Society) series.

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