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Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

Riccardo Porreca 1 Eugenio Cinquemani 2, * John Lygeros 3 Giancarlo Ferrari-Trecate 1
* Corresponding author
2 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
Abstract : We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of [1] that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae.
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https://hal.inria.fr/hal-00793026
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Submitted on : Thursday, February 21, 2013 - 2:33:02 PM
Last modification on : Thursday, May 27, 2021 - 1:54:05 PM

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Riccardo Porreca, Eugenio Cinquemani, John Lygeros, Giancarlo Ferrari-Trecate. Structural identification of unate-like genetic network models from time-lapse protein concentration measurements. Proceedings of the 49th IEEE Conference on Decision and Control (CDC 2010), 2010, Atlanta, United States. pp.2529-2534, ⟨10.1109/CDC.2010.5717922⟩. ⟨hal-00793026⟩

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