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Inference of size dependence of transcription parameters from single cell data using multi-scale models of gene expression

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SDBW03 - Advances in numerical and analytic approaches for the study of non-spatial stochastic dynamical systems in molecular biology

Co-authors: Anthony Bowman (Imperial College London), Xi-Ming Sun (MRC CSC ), Samuel Marguerat (MRC CSC )

Gene expression is affected by both random timing of reactions (intrinsic noise) and interaction with global stochastic systems in the cells (extrinsic noise). A challenge in inferring parameters of gene expression using models of stochastic gene expression is that these models usually only inlcude intrinsic noise. However, experimental distributions of transcripts are strongly influenced by extrinsic effects including cell cycle and cell division. Here, we present a multi-scale approach in stochastic gene expression to deal with this problem. We apply our methodology to data obtained using single molecule Fish technique in fission yeast. The data suggests cell size influences transcription parameters. We use Approximate Bayesian Computation (ABC) along with sequential Monte Carlo to infer the dependence of gene expression parameters on cell size. Our analysis reveals a linear increase of transcription burst size during the cell cycle.

This talk is part of the Isaac Newton Institute Seminar Series series.

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