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

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dc.article.number108607
dc.catalogadorvzp
dc.contributor.authorAltamirano Muñoz, Alvaro Sebastian
dc.contributor.authorTapia García, Ignacio Tomas
dc.contributor.authorAcuña, Vicente
dc.contributor.authorGarrido Cortes, Daniel
dc.contributor.authorSaa Higuera, Pedro
dc.date.accessioned2025-08-26T15:52:54Z
dc.date.available2025-08-26T15:52:54Z
dc.date.issued2026
dc.description.abstractElementary 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.
dc.format.extent10 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.compbiolchem.2025.108607
dc.identifier.eissn1476-9271
dc.identifier.urihttps://doi.org/10.1016/j.compbiolchem.2025.108607
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/105281
dc.information.autorucEscuela de Ingeniería; Garrido Cortes, Daniel; 0000-0002-4982-134X; 226814
dc.information.autorucEscuela de Ingeniería; Altamirano Muñoz, Alvaro Sebastian; S/I; 1092272
dc.information.autorucEscuela de Ingeniería; Tapia García, Ignacio Tomas; 0009-0006-5567-0038; 1084915
dc.information.autorucEscuela de Ingeniería; Saa Higuera, Pedro; 0000-0002-1659-9041; 162204
dc.language.isoen
dc.nota.accesocontenido parcial
dc.rightsacceso restringido
dc.subjectGenome-scale model
dc.subjectMetabolic conversion
dc.subjectCross-feeding
dc.subjectEcological interactions
dc.subjectMicrobial communities
dc.subject.ddc570
dc.subject.deweyBiologíaes_ES
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleMETACONE: A scalable framework for exploring the conversion cone of metabolic networks
dc.typeartículo
dc.volumen120
sipa.codpersvinculados226814
sipa.codpersvinculados1092272
sipa.codpersvinculados1084915
sipa.codpersvinculados162204
sipa.trazabilidadORCID;2025-08-22
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