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Probability metrics to calibrate stochastic chemical kinetics

Koeppl, H. and Setti, G. and Pelet, S. and Mangia, M. and Petrov, T. and Peter, M. (2010):
Probability metrics to calibrate stochastic chemical kinetics.
In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, [Article]

Abstract

Calibration or model parameter estimation from measured data is an ubiquitous problem in engineering. In systems biology this problem turns out to be particularly challenging due to very short data-records, low signal-to-noise ratio of data acquisition, large intrinsic process noise and limited measurement access to only a few, of sometimes several hundreds, state variables. We review state-of-the-art model calibration techniques and also discuss their relation to the general reverse-engineering problem in systems biology. For biomolecular circuits involving low-copy-number molecules we adopt a Markov process setup and discuss a calibration approach based on suitable metrics between probability measures and propose the metrics computation for the multivariate case. In particular, we use Kantorovich's distance and devise an algorithm, for the case when FACS (fluorescence-activated cell sorting) measurements are given. We discuss a case study involving FACS data for the high-osmolarity glycerol (HOG) pathway in budding yeast.

Item Type: Article
Erschienen: 2010
Creators: Koeppl, H. and Setti, G. and Pelet, S. and Mangia, M. and Petrov, T. and Peter, M.
Title: Probability metrics to calibrate stochastic chemical kinetics
Language: English
Abstract:

Calibration or model parameter estimation from measured data is an ubiquitous problem in engineering. In systems biology this problem turns out to be particularly challenging due to very short data-records, low signal-to-noise ratio of data acquisition, large intrinsic process noise and limited measurement access to only a few, of sometimes several hundreds, state variables. We review state-of-the-art model calibration techniques and also discuss their relation to the general reverse-engineering problem in systems biology. For biomolecular circuits involving low-copy-number molecules we adopt a Markov process setup and discuss a calibration approach based on suitable metrics between probability measures and propose the metrics computation for the multivariate case. In particular, we use Kantorovich's distance and devise an algorithm, for the case when FACS (fluorescence-activated cell sorting) measurements are given. We discuss a case study involving FACS data for the high-osmolarity glycerol (HOG) pathway in budding yeast.

Journal or Publication Title: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on
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
Date Deposited: 04 Apr 2014 12:33
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