Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions

Abstract

Constraint-based modeling methods, such as Flux Balance Analysis (FBA), have been extensively used to decipher complex, information rich -omics datasets to elicit system-wide behavioral patterns of cellular metabolism. FBA has been successfully used to gain insight in a wide range of applications, such as range of substrate utilization, product yields and to design metabolic engineering strategies to improve bioprocess performance. A well-known challenge associated with large genome-scale metabolic networks is that they result in underdetermined problem formulations.Consequently, rather than unique solutions, FBA and related methods examine ranges of reaction flux values that are consistent with the studied physiological conditions. The wider the reported flux ranges, the higher the uncertainty in the determination of basic reaction properties, limiting interpretability of and confidence in the results. Herein, we propose a new, computationally efficient approach that refines flux range predictions by constraining reaction fluxes on the basis of the elemental balance of carbon. We compared carbon constraint FBA (ccFBA) against experimentally-measured intracellular fluxes using the latest CHO GEM (iCHO1766) and were able to substantially improve the accuracy of predicted flux values compared with FBA. ccFBA can be used as a stand-alone method but is also compatible with and complimentary to other constraint-based approaches. K E Y W O R D S carbon constraining, CHO, constraint-based modeling, FBA, genome-scale metabolic network

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

“…Other diverse constraints have also been applied to genome-scale models, beyond the metabolic constraints of determined uptake and production rates. For example, the elemental balance of carbon through intracellular reactions was introduced as additional constraints [36]. Sánchez et al (2017) incorporated enzymatic constraints in a yeast genome-scale metabolic model to ensure each metabolic flux does not exceed its maximum capacity, significantly reducing flux variability of model simulations [37].…”

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

“…Few other studies also tried to improve the predictions by iCHO1766. For example, Lularevic et al [55] reduced variability in flux variability analysis by adding carbon availability constraints. In another study, the predictions of intracellular fluxes were improved by adding constraints based on enzyme kinetic information [9].…”

Guided extraction of genome-scale metabolic models for the integration and analysis of omics data

“…However, there are certain limitations. First, FBA does not yield a unique solution and is highly dependent on the choice of the objective function, i.e., a description of the phenotype relevant to the problem being studied [28] . While in some cases biomass optimisation is a plausible biological objective (e.g., cancer cells, cell lines, single cell organisms), different optimisation criteria need to be applied depending on the question of interest.…”