On Dynamically Generating Relevant Elementary Flux Modes in a Metabolic Network using Optimization

TL;DR

This paper introduces an optimization-based method for dynamically generating relevant elementary flux modes (EFMs) in metabolic networks, enabling efficient flux analysis without enumerating all EFMs, especially in complex networks.

Contribution

The authors propose a novel optimization approach that selectively generates EFMs contributing to the solution, reducing computational costs in EFMs-based metabolic flux analysis.

Findings

Method effectively solves EFMs-based MFA with fewer EFMs.

Applicable to complex networks with low computational cost.

Validated using data from Chinese hamster ovary cell culture.

Abstract

Elementary flux modes (EFMs) are pathways through a metabolic reaction network that connect external substrates to products. Using EFMs, a metabolic network can be transformed into its macroscopic counterpart, in which the internal metabolites have been eliminated and only external metabolites remain. In EFMs-based metabolic flux analysis (MFA) experimentally determined external fluxes are used to estimate the flux of each EFM. It is in general prohibitive to enumerate all EFMs for complex networks, since the number of EFMs increases rapidly with network complexity. In this work we present an optimization-based method that dynamically generates a subset of EFMs and solves the EFMs-based MFA problem simultaneously. The obtained subset contains EFMs that contribute to the optimal solution of the EFMs-based MFA problem. The usefulness of our method was examined in a case-study using data from a Chinese hamster ovary cell culture and two networks of varied complexity. It was demonstrated that the EFMs-based MFA problem could be solved at a low computational cost, even for the more complex network. Additionally, only a fraction of the total number of EFMs was needed to compute the optimal solution.