Toward Systems Biology

May 30 - 31, June 1, 2011


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Accounting for Extrinsic Variability in the Estimation of Stochastic Rate Constants

Single-cell recordings of transcriptional and post-transcriptional processes reveal the inherent stochasticity of cellular events. However, to a large extent the observed variability in isogenic cell populations is due to extrinsic factors, such as difference in expression capacity, cell volume and cell cycle stage - to name a few. Thus, such experimental data represents a convolution of effects from stochastic kinetics and extrinsic noise sources. Recent parameter inference schemes for single-cell data just account for variability due to molecular noise. Here we present a Bayesian inference scheme which de-convolutes the two sources of variability and enables us to obtain optimal estimates of stochastic rate constants of low copy-number events and extract statistical information about cell-to-cell variability. Moreover, we can reconstruct time-courses of unobserved species. We apply the scheme to a model of the osmo-stress induced transcriptional activation in budding yeast.

Heinz Koeppl, ETH Zurich