Structural identification of biochemical reaction networks from population snapshot data

Eugenio Cinquemani 1
1 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 : In this paper we investigate how randomness in biochemical network dynamics improves identification of the network structure. Focusing on the case of so-called population snapshot data, we set out the problem as that of reconstructing the unknown stoichiometry matrix and rate parameters of the network in the case of state-affine reaction rates. We discuss what additional information is conveyed by the observation of second-order moments of the system species relative to the sole knowledge of their mean profiles. We then illustrate the impact of this additional piece of information in the reconstruction of an unknown network structure by means of a simple numerical example.
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Soumis le : samedi 26 août 2017 - 13:15:20
Dernière modification le : mercredi 11 avril 2018 - 01:53:24


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  • HAL Id : hal-01577565, version 1



Eugenio Cinquemani. Structural identification of biochemical reaction networks from population snapshot data. Proceedings of the 20th IFAC World Congress, Jul 2017, Toulouse, France. 2017, 〈〉. 〈hal-01577565〉



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