Toward Systems Biology
May 30 - 31, June 1, 2011
Grenoble
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