(58f) An Improved Algorithm for Flux Variability Analysis

Flux balance analysis (FBA) [1] is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) [2] is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving 2n+1 linear programs (LPs), with n being the number of reactions in the metabolic network which can be computationally burdensome for genome scale metabolic models [3].

In this presentation we describe a new algorithm for FVA that can solve in less than 2n+1 LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.

[1] - M. R. Watson. Metabolic maps for the Apple II. Biochemical Society Transactions, 12(6):1093–1094, 12 1984.

[2] - Anthony P Burgard, Shankar Vaidyaraman, and Costas D Maranas. Minimal reaction sets for escherichia coli metabolism under different growth requirements and uptake environments. Biotechnology progress, 17(5):791–797, 2001.

[3] - Marouen Ben Guebila. Vffva: dynamic load balancing enables large-scale flux variability analysis. BMC bioinformatics, 21(1):1–13, 2020.