Global Sensitivity Analysis in Metabolic Networks

Abstract

In this work, we have performed a global sensitivity analysis through variance-based techniques to identify which parameters have the largest impact on model output and which of them account for most of the uncertainty in that output. Sensitivity indices have been calculated for each
parameter, based on Sobol’s approach (2001), which makes use of Monte Carlo methods. The global sensitivity analysis has been carried out on a dynamic model for the Embden-Meyerhof-Parnas pathway, the phosphotransferase system and the pentose phosphate pathway of Escherichia coli K-12
strain W3110. The model comprises eighteen dynamic mass balance equations for extracellular glucose and intracellular metabolites, twenty nine kinetic rate expressions and seven additional algebraic equations to represent the concentration of co-metabolites. The model involves around one hundred parameters. Each parameter has been considered to have a normal probability distribution centered on its nominal value and sample sizes of one thousand scenarios have been considered. The preceding analysis has allowed identification of less than twenty parameters as the most influential
ones on the complex metabolic network under study.