METACONE: A scalable framework for exploring the conversion cone of metabolic networks

Abstract

Elementary Conversion Modes (ECMs) – a subset of Elementary Flux Modes (EFMs) – capture the entire production/consumption potential of a metabolic network, providing a more practical view of its interactions with the environment. Despite its reduced size, the set of ECMs is too large for exhaustive enumeration in models reaching genome scale. To address this limitation, we have developed METACONE (METAbolic Conversion cOne for Network Exploration), a scalable algorithm for the computation of a representative linear basis of the conversion cone, the subspace in which all ECMs lie. Two METACONE variants are proposed based on the solution of a series of linear programs following different heuristics. We evaluated the performance of the variants on metabolic models of different sizes, demonstrating their scalability. We further analyzed the resulting basis to explore metabolic capabilities under different environmental conditions in Escherichia coli, identifying metabolic patterns consistent with reported data. Finally, we applied the algorithm to explore metabolic interactions in a microbial consortium of Phocaeicola dorei and Lachnoclostridium symbiosum, recapitulating known cross-feeding interactions and suggesting new possibilities. We envision METACONE as a valuable tool for understanding microbial metabolism in increasingly complex consortia while addressing current scalability challenges.