Characterizing transcriptional transition states in cell differentiation (360G-Wellcome-204004_Z_16_A)
As stem cells differentiate, their transcription profiles change over time. These complex dynamics are essential for generating specialised cell types and facilitating normal development. I aim to characterise the movement of these differentiating cells through gene expression space using a multidisciplinary approach. I will use stochastic models to simulate stem cell dynamics over developmental time, and fit these models to transcriptomic data using Bayesian methods (Approximate Bayesian Computation and Particle Markov Chain Monte Carlo). My approach also aims to incorporate structural information from Genome Architecture Mapping experiments, to further improve these models. This work will improve the characterisation of key transition states within stem cell dynamics, and lead to more informative models of cell differentiation.
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