Biochemical neutral solutions using S-system models
University of Texas M.D.Anderson Cancer Center –
One of the major difficulties of modeling biological systems from time series is the identification of a parameter set which gives the model the same dynamical behavior of the data. A more austere goal is the identification of the biochemical interaction of the systems components from the model parameters. In this talk, we present a method for the S-System parameter space identification from biological time series based on Monte Carlo process and a parameter optimization algorithm. The proposed methodology was applied to real time series data from the glycolytic pathway of the bacteria Lactococcus latis and ensembles of models with different network topologies were generated. The parameter optimization algorithm was also successfully applied to the same dynamical data however imposing a pre-specified network topology from previous knowledge, foreseeing the method as an exploration tool for test of hypothesis and design of new experiments.
Date: 2008-Dec-03 Time: 15:00:00 Room: 04
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INESC-ID ESR Talks – February 2023
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