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Structural identification of unate-like genetic network models from time-lapse protein concentration measurements
2010
49th IEEE Conference on Decision and Control (CDC)
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
doi:10.1109/cdc.2010.5717922
dblp:conf/cdc/PorrecaCLF10
fatcat:wmsfuqw4ajhdpl2pkhiehces4e