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Communication Dans Un Congrès Année : 2013

Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference

Résumé

Experimental techniques in biology such as microfluidic devices and time-lapse microscopy allow tracking of the gene expression in single cells over time. So far, few attempts have been made to fully exploit these data for modeling the dynamics of biological networks in cell populations. In this paper we compare two modeling approaches capable to describe cell-to-cell variability: Mixed-Effects (ME) models and the Chemical Master Equation (CME).We discuss how network parameters can be identified from experimental data and use real data of the HOG pathway in yeast to assess model quality. For CME we rely on the identification approach proposed by Zechner et al. (PNAS, 2012), based on moments of the probability distribution involved in the CME. ME and moment-based (MB) inference will be also contrasted in terms of general features and possible uses in biology.
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Dates et versions

hal-00831641 , version 1 (07-06-2013)

Identifiants

  • HAL Id : hal-00831641 , version 1

Citer

Andres M. Gonzalez-Vargas, Jannis Uhlendorf, Joé Schaul, Eugenio Cinquemani, Grégory Batt, et al.. Identification of biological models from single-cell data: a comparison between mixed-effects and moment-based inference. ECC - 12th European Control Conference - 2013, Jul 2013, Zürich, Switzerland. ⟨hal-00831641⟩
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