(431b) Assessing the Importance of Parametric Uncertainty on Flux Balance Analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass composition neither changes in response to growth conditions nor environmental/genetic perturbations, and (ii) equal metabolite production and consumption rates (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in (i) biomass equation coefficients, and (ii) departures from steady-state due to temporal fluctuations propagate to FBA results. In both cases, conditional sampling of parameter space is required to (i) re-weigh the biomass equation so as the molecular weight remains equal to 1 g/mmol, and (ii) ensure metabolite pool conservation even under temporally varying conditions. Our results confirm that the FBA-predicted growth rate and the underlying flux distributions are highly dependent on whether sampling is conducted with or without including the aforementioned constraints. Overall, uncertainty-to-variation of FBA predictions reported here can be interpreted as a cautionary tale and the framework presented in this work can be used for applications where temporal or experimentally derived parameter variability in FBA needs to be assessed.