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Optimal Variational Perturbations for the Inference of Stochastic Reaction Dynamics


C. Zechner, P. Nandy, M. Unger, H. Koeppl

IEEE Conference on Decision and Control, Maui, pp. 5336-5341

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.


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% Autogenerated BibTeX entry
@InProceedings { ZecEtal:2012:IFA_4634,
    author={C. Zechner and P. Nandy and M. Unger and H. Koeppl},
    title={{Optimal Variational Perturbations for the Inference of
	  Stochastic Reaction Dynamics}},
    booktitle={IEEE Conference on Decision and Control},
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