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Reverse mathematical methods for reconstructing molecular dynamics in single cell

Modeling fluctuations and large deviations in stochastic gene expression

speaker: Rahul Kulkarni (Univ. of Massachusetts, Boston)

abstract: In several biological systems, phenotypic variations are seen even among genetically identical cells in homogeneous environments. Recent research indicates that such `non-genetic individuality' can be driven by rare events arising from the intrinsic stochasticity of gene expression. Characterizing the fluctuations that give rise to such rare events motivates the analysis of large deviations in stochastic models of gene expression. In this talk, I will discuss analytical approaches developed by my group for general stochastic models of gene expression. We combine approaches from queueing theory and non-equilibrium statistical mechanics to characterize large deviations and driven processes for general models of gene expression. Modeling gene expression as a Batch Markovian Arrival Process (BMAP), we derive exact analytical results quantifying large deviations of time-integrated random variables such as promoter activity fluctuations. The results obtained can be used to quantify the likelihood of large deviations, to characterize system fluctuations conditional on rare events and to identify combinations of model parameters that can give rise to dynamical phase transitions in system dynamics. We apply the formalism developed to gain insights into the impact of post-transcriptional regulation on fluctuations and rare events in stochastic models of gene expression.

Thu 18 Oct, 14:30 - 15:15, Aula Dini
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