Global Sensitivity Analysis of Constraint-Based Metabolic Models

In the latter years, detailed genome-wide metabolic models have been proposed, paving the way to thorough investigations of the connection between genotype and phenotype in human cells. Nevertheless, classic modeling and dynamic simulation approaches—based either on differential equations integration, Markov chains or hybrid methods—are still unfeasible on genome-wide models due to the lack of detailed information about kinetic parameters and initial molecular amounts. By relying on a steady-state assumption and constraints on extracellular fluxes, constraint-based modeling provides an alternative means—computationally less expensive than dynamic simulation—for the investigation of genome-wide biochemical models. Still, the predictions provided by constraint-based analysis methods (e.g., flux balance analysis) are strongly dependent on the choice of flux boundaries. To contain possible errors induced by erroneous boundary choices, a rational approach suggests to focus on the pivotal ones. In this work we propose a novel methodology for the automatic identification of the key fluxes in large-scale constraint-based models, exploiting variance-based sensitivity analysis and distributing the computation on massively multi-core architectures. We show a proof-of-concept of our approach on core models of relatively small size (up to 314 reactions and 256 chemical species), highlighting the computational challenges.