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Optimal variational perturbations for the inference of stochastic reaction dynamics

Zechner, C. and Nandy, P. and Unger, M. and Koeppl, H. (2012):
Optimal variational perturbations for the inference of stochastic reaction dynamics.
IEEE, In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Abstract

Although single-cell techniques are advancing rapidly, quantitative assessment of kinetic parameters is still characterized by ill-posedness and a large degree of uncertainty. In many standard experiments, where transcriptional activation is recorded upon application of a step-like external perturbation, cells almost instantaneously adapt such that only a few informative measurements can be obtained. Consequently, the information gain between subsequent experiments or time points is comparably low, which is reflected in a hardly decreasing parameter uncertainty. However, novel microfluidic techniques can be applied to synthesize more sophisticated perturbations to increase the informativeness of such time-course experiments. Here we introduce a mathematical framework to design optimal perturbations for the inference of stochastic reaction dynamics. Based on Bayesian statistics, we formulate a variational problem to find optimal temporal perturbations and solve it using a stochastic approximation algorithm. Simulations are provided for the realistic scenario of noisy and discrete-time measurements using two simple reaction networks.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Zechner, C. and Nandy, P. and Unger, M. and Koeppl, H.
Title: Optimal variational perturbations for the inference of stochastic reaction dynamics
Language: English
Abstract:

Although single-cell techniques are advancing rapidly, quantitative assessment of kinetic parameters is still characterized by ill-posedness and a large degree of uncertainty. In many standard experiments, where transcriptional activation is recorded upon application of a step-like external perturbation, cells almost instantaneously adapt such that only a few informative measurements can be obtained. Consequently, the information gain between subsequent experiments or time points is comparably low, which is reflected in a hardly decreasing parameter uncertainty. However, novel microfluidic techniques can be applied to synthesize more sophisticated perturbations to increase the informativeness of such time-course experiments. Here we introduce a mathematical framework to design optimal perturbations for the inference of stochastic reaction dynamics. Based on Bayesian statistics, we formulate a variational problem to find optimal temporal perturbations and solve it using a stochastic approximation algorithm. Simulations are provided for the realistic scenario of noisy and discrete-time measurements using two simple reaction networks.

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: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
Date Deposited: 04 Apr 2014 11:41
Official URL: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...
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