Identification of Dynamic BioModels Based on Sensitivity Analysis

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The research to be presented will focus on the robust identification of dynamic metabolic flux models based on parametric sensitivity analysis. The particular case study that is chosen to illustrate the proposed method is Diauxic growth in Escherichia coli in a batch culture. This approach intends to show how to identify the model parameters of the dynamic model based on a parametric sensitivity analysis that explicitly accounts for correlations in the data. The sensitivity is quantified by a parameter sensitivity spectrum. Then, the parameters are ranked based on this analysis to assess whether a subset of the parameters can be eliminated from further analysis. Finally, identification of the remaining significant parameters is based on the maximization of an overall parametric sensitivity measure subject to set based constraints that are derived from the available data. The parametric sensitivity method is global in the sense that it examines the simultaneous variation of all the model's outputs instead of focusing on outputs variables one at a time.