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**Zechner, C. and Nandy, P. and Unger, M. and Koeppl, H.** (2012):

*Optimal variational perturbations for the inference of stochastic reaction dynamics.*

pp. 5336-5341, IEEE, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), [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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