https://hal.inria.fr/hal-00763439Kelk, Steven MSteven MKelkOlivier, Brett GBrett GOlivierStougie, LeenLeenStougieDepartment of mathematics and computing science [Eindhoven] - TU/e - Eindhoven University of Technology [Eindhoven]Bruggeman, Frank JFrank JBruggemanOptimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks.HAL CCSD2012[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS][INFO.INFO-CC] Computer Science [cs]/Computational Complexity [cs.CC][INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM][SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]Sagot, Marie-France2012-12-10 18:25:372020-05-29 06:56:012012-12-10 18:25:37enJournal articles10.1038/srep005801The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states.