Using metabolic networks to predict cross-feeding and competition interactions between microorganisms

dc.catalogadorjca
dc.contributor.authorSilva-Andrade, Claudia
dc.contributor.authorRodriguez-Fernández, María
dc.contributor.authorGarrido, Daniel
dc.contributor.authorMartin, Alberto J. M.
dc.contributor.authorJensen, Paul A.
dc.date.accessioned2024-03-27T13:32:08Z
dc.date.available2024-03-27T13:32:08Z
dc.date.issued2024
dc.description.abstractUnderstanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.
dc.fechaingreso.objetodigital2024-08-27
dc.fuente.origenORCID
dc.identifier.doi10.1128/spectrum.02287-23
dc.identifier.issn2165-0497
dc.identifier.urihttps://doi.org/10.1128/spectrum.02287-23
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/84804
dc.information.autorucEscuela de Ingeniería; Garrido Cortes, Daniel; 0000-0002-4982-134X; 226814
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaMicrobiology Spectrum
dc.rightsacceso abierto
dc.subjectBacterial interaction
dc.subjectCross-feeding
dc.subjectCompetition
dc.subjectMachine learning
dc.subject.ddc570
dc.subject.deweyBiologíaes_ES
dc.titleUsing metabolic networks to predict cross-feeding and competition interactions between microorganisms
dc.typeartículo
sipa.codpersvinculados226814
sipa.trazabilidadORCID;2024-03-25
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