Balancing biomass reaction stoichiometry and measured fluxes in flux balance analysis

Motivation Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty.Results In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes.Availability and implementation The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

Automated summary

In this study, a method is presented to modify the stoichiometry of the biomass reaction in order to make the flux balance analysis problem feasible and improve the accuracy of the model. Realistic flux measurements of E.coli are used to demonstrate the value of this approach, highlighting the overestimation of growth-associated maintenance (GAM) demand of ATP in recent genome-scale models.