A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information

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dc.contributor.authorSilva-Andrade, Claudia
dc.contributor.authorHernández, Sergio
dc.contributor.authorSaa Higuera, Pedro
dc.contributor.authorPérez-Rueda, Ernesto
dc.contributor.authorGarrido Cortés, Daniel
dc.contributor.authorMartin, Alberto J.
dc.date.accessioned2025-06-09T15:03:10Z
dc.date.available2025-06-09T15:03:10Z
dc.date.issued2025
dc.description.abstractUnderstanding the behavior of microbial consortia is crucial for predicting metabolite production by microorganisms. Genome-scale network reconstructions enable the computation of metabolic interactions and specific associations within microbial consortia underpinning the production of different metabolites. In the context of the human gut, butyrate is a central metabolite produced by bacteria that plays a key role within the gut microbiome impacting human health. Despite its importance, there is a lack of computational methods capable of predicting its production as a function of the consortium composition. Here, we present a novel machine-learning approach leveraging automatically generated genome-scale metabolic models to tackle this limitation. Briefly, all consortia made of two up to 13 members from a pool of 19 bacteria with known genomes, including at least one butyrate producer from a pool of three known producer species, were built and their (maximum) in silico butyrate production simulated. Using network-derived descriptors from each bacteria, butyrate production by the above consortia was used as training data for various machine learning models. The performance of the algorithms was evaluated using k-fold cross-validation and new experimental data, displaying a Pearson correlation coefficient exceeding 0.75 for the predicted and observed butyrate production in two bacteria consortia. While consortia with more than two bacteria showed generally worse predictions, the best machine-learning models still outperformed predictions from genome-scale metabolic models alone. Overall, this approach provides a valuable tool and framework for probing promising butyrate-producing consortia on a large scale, guiding experimentation, and more importantly, predicting metabolic production by consortia.
dc.format.extent16 páginas
dc.fuente.origenORCID
dc.identifier.doi10.7717/peerj.19296
dc.identifier.urihttps://doi.org/10.7717/peerj.19296
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104616
dc.information.autorucEscuela de Ingeniería; Garrido Cortés, Daniel; 0000-0002-4982-134X; 226814
dc.information.autorucEscuela de Ingeniería; Saa Higuera, Pedro; 0000-0002-1659-9041; 162204
dc.language.isoen
dc.nota.accesocontenido completo
dc.rightsacceso abierto
dc.rights.licenseCC BY Atribución Internacional 4.0
dc.rights.urihttps://www.creativecommons.org/licenses/by/4.0/
dc.subjectMicrobial consortia
dc.subjectMachine learning
dc.subjectButyrate production
dc.subjectMetabolic network
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information
dc.typeartículo
sipa.codpersvinculados226814
sipa.codpersvinculados162204
sipa.trazabilidadORCID;2025-06-03
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