Thermodynamics-based Significance Ranking of Candidates for Metabolomics
Integration of experimental measurements of intracellular metabolite concentrations in genome scale models restricts the thermodynamically feasible flux space and reduces uncertainty regarding the net outcome of by-directional reactions. By combining tFBA, Marcov Chain sampling, Experimental Design and Global Sensitivity Analysis we present an efficient algorithm to quantify the effect intracellular metabolites have on the thermodynamic flexibility of cellular metabolism. Metabolites are ranked based on the extent by which they reduce the thermodynamically feasible flux space when fixed at any value within their respective feasible bounds. The proposed methodology effectively defines the minimal amount of experimental information required to precisely describe the state of cellular metabolism (i.e the flux distribution) and provides a ranked list of targets for metabolomics.