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Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations

Zechner, C. and Pelet, S. and Peter, M. and Koeppl, H. (2011):
Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations.
In: IEEE Conference on Decision and Control and European Control Conference, IEEE, pp. 5837-5843, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

Abstract

Robust estimation of kinetic parameters of intracellular processes requires large amounts of quantitative data. Due to the high uncertainty of such processes and the fact that recent single-cell measurement techniques have limited resolution and dimensionality, estimation should pool recordings of multiple cells of an isogenic cell population. However, experimental results have shown that several factors such as cell volume or cell-cycle stage can drastically affect signaling as well as protein expression, leading to inherent heterogeneities in the cell population measurements. Here we present a recursive Bayesian estimation procedure for stochastic kinetic model calibration using heterogeneous cell population data. While obtaining optimal estimates for the rate constants, this approach allows to reconstruct missing species as well as to quantitatively capture extrinsic variability. The proposed algorithm is applied to a model of the osmo-stress induced MAPK Hog1 activation in the cytoplasm and its translocation to the nucleus.

Item Type: Article
Erschienen: 2011
Creators: Zechner, C. and Pelet, S. and Peter, M. and Koeppl, H.
Title: Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations
Language: German
Abstract:

Robust estimation of kinetic parameters of intracellular processes requires large amounts of quantitative data. Due to the high uncertainty of such processes and the fact that recent single-cell measurement techniques have limited resolution and dimensionality, estimation should pool recordings of multiple cells of an isogenic cell population. However, experimental results have shown that several factors such as cell volume or cell-cycle stage can drastically affect signaling as well as protein expression, leading to inherent heterogeneities in the cell population measurements. Here we present a recursive Bayesian estimation procedure for stochastic kinetic model calibration using heterogeneous cell population data. While obtaining optimal estimates for the rate constants, this approach allows to reconstruct missing species as well as to quantitatively capture extrinsic variability. The proposed algorithm is applied to a model of the osmo-stress induced MAPK Hog1 activation in the cytoplasm and its translocation to the nucleus.

Journal or Publication Title: IEEE Conference on Decision and Control and European Control Conference
Place of Publication: Orlando, Florida
Publisher: IEEE
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 04 Apr 2014 11:41
Official URL: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...
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