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Marginal dynamics of stochastic biochemical networks in random environments

Zechner, C. and Deb, S. and Koeppl, H. (2013):
Marginal dynamics of stochastic biochemical networks in random environments.
IEEE, In: 2013 European Control Conference (ECC), Zürich, 2013, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669606],
[Conference or Workshop Item]

Abstract

Stochastic simulation algorithms provide a powerful means to understand complex biochemical processes as well as to solve the inverse problem of reconstructing hidden states and parameters from experimental single-cell data. At presence, a repertoire of efficient algorithms for simulating and calibrating stochastic reaction networks is available. However, most of these approaches do not account for the fact that each cell of a clonal population is exposed to a random extrinsic environment, i.e., the agglomerate of so-called extrinsic factors such as cell size, shape or cell cycle stage. We recently proposed a dynamic description of stochastic chemical kinetics in random but unknown extrinsic environments, reflected by a stochastic process where uncertain parameters are marginalized out. In this work we further investigate that process and provide additional analytical results. We demonstrate the marginalization using several biologically relevant parameter distributions and derive exact waiting-time distributions. We further show that the marginalized process model can achieve a variance reduction in the context of parameter inference.

Item Type: Conference or Workshop Item
Erschienen: 2013
Creators: Zechner, C. and Deb, S. and Koeppl, H.
Title: Marginal dynamics of stochastic biochemical networks in random environments
Language: English
Abstract:

Stochastic simulation algorithms provide a powerful means to understand complex biochemical processes as well as to solve the inverse problem of reconstructing hidden states and parameters from experimental single-cell data. At presence, a repertoire of efficient algorithms for simulating and calibrating stochastic reaction networks is available. However, most of these approaches do not account for the fact that each cell of a clonal population is exposed to a random extrinsic environment, i.e., the agglomerate of so-called extrinsic factors such as cell size, shape or cell cycle stage. We recently proposed a dynamic description of stochastic chemical kinetics in random but unknown extrinsic environments, reflected by a stochastic process where uncertain parameters are marginalized out. In this work we further investigate that process and provide additional analytical results. We demonstrate the marginalization using several biologically relevant parameter distributions and derive exact waiting-time distributions. We further show that the marginalized process model can achieve a variance reduction in the context of parameter inference.

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
Event Title: 2013 European Control Conference (ECC)
Event Location: Zürich
Event Dates: 2013
Date Deposited: 04 Apr 2014 11:41
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669606
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