Zechner, C. ; Nandy, P. ; Unger, M. ; Koeppl, H. (2012)
Optimal variational perturbations for the inference of stochastic reaction dynamics.
2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Zechner, C. ; Nandy, P. ; Unger, M. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Optimal variational perturbations for the inference of stochastic reaction dynamics |
| Sprache: | Englisch |
| Publikationsjahr: | Dezember 2012 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) |
| URL / URN: | http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe... |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 04 Apr 2014 11:41 |
| Letzte Änderung: | 23 Sep 2021 14:31 |
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