University of Cambridge > > Statistical Laboratory Graduate Seminars > Stochastic Modelling of Nanog-Oct4-Sox2 Gene Regulatory Network

Stochastic Modelling of Nanog-Oct4-Sox2 Gene Regulatory Network

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Embryonic Stem (ES) cells have the unique property of being able to self-renew indefinitely and differentiate to all three germ layers (endoderm, mesoderm, ectoderm). Understanding the factors that determine the ability of ES cells to maintain their pluripotency is crucial in stem cell research. Using flow cytometry data of Nanog expression levels within a single cell. We model the distributions of Nanog expression levels over time to understand the role of transcriptional fluctuation in maintaining the pluripotent state of ES cells.

We use phase plane analysis, bifurcation theory and stability analysis to understand how noise affects the dynamics of different systems (such as bistable systems or excitable systems), in comparison to deterministic models. Further, we apply Approximate Bayesian Computation (ABC) schemes to validate our suggested model and infer parameters. With this, we may make predictions about the nature of the biological processes in a manner that may be tested experimentally.

This talk is part of the Statistical Laboratory Graduate Seminars series.

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