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

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. Here, we compare two modeling approaches capable to describe cell-to-cell variability: mixed-effects (ME) models and the chemical master equation (CME). They represent two extreme cases in which all the variability comes either from extrinsic or intrinsic noise. We discuss how model parameters can be identified from experimental data using a moment-closure approach for CME and the stochastic approximation of expectation-maximization algorithm for ME models. Then, we use time-lapse movie data to identify parameter values for models of the HOG pathway in yeast and compare the prediction quality of the two models on validation data. Work done in collaboration with Andres Gonzalez, Jannis Uhlendorf, Joe Schaul, Eugenio Cinquemani, Pascal Hersen and Giancarlo Ferrari-Trecate.

Type of Seminar:
IfA Seminar
Dr. Gregory Batt
Inria, Public Science and Technology Institution, Paris-Rocquencourt, France
Mar 27, 2013   15:15

LFV E 41
Contact Person:

Prof. Heinz Koeppl
No downloadable files available.
Biographical Sketch:
Gregory Batt is a research scientist at INRIA Paris-Rocquencourt. He completed a BSc in molecular biology and an MSc in computer science at Ecole Normale Supérieure de Lyon and Grenoble University. In 2006 he obtained a PhD degree in computer science from Grenoble University. In 2006 and 2007, he worked as a postdoctoral researcher at Boston University and at the Verimag research center. Since 2007 he held a tenured position at INRIA in the Contraintes research group. His research interests include the development and application of computational methods for analysis and control of natural biological systems and design and optimization of novel, useful biological systems.