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Unravelling the web of dark interactions: Explainable inference of the diversity of microbial interactions

hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBARROSO-BERGADA, Didac
hal.structure.identifierUniversity of Surrey [UNIS]
dc.contributor.authorTAMADDONI-NEZHAD, Alireza
hal.structure.identifierUniversity of Surrey [UNIS]
dc.contributor.authorVARGHESE, Dany
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVACHER, Corinne
hal.structure.identifierSyngenta AG Suisse
dc.contributor.authorGALIC, Nika
hal.structure.identifierBIOlogie et GEstion des Risques en agriculture [BIOGER]
dc.contributor.authorLAVAL, Valérie
hal.structure.identifierBIOlogie et GEstion des Risques en agriculture [BIOGER]
dc.contributor.authorSUFFERT, Frédéric
hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBOHAN, David
dc.date.accessioned2024-04-11T08:05:45Z
dc.date.available2024-04-11T08:05:45Z
dc.date.issued2023
dc.identifier.issn0065-2504
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/197466
dc.description.abstractEnThe functional diversity of microbial communities emerges from a combination of the great number of species and the many interaction types, such as competition, mutualism, predation or parasitism, in microbial ecological networks. Understanding the relationship between microbial networks and the functions delivered by the microbial communities is a key challenge for microbial ecology, particularly as so many of these interactions are difficult to observe and characterise. We believe that this ’Dark Web’ of interactions could be unravelled using an explainable machine learning approach, called Abductive/Inductive Logic Programming (A/ILP) in the R package InfIntE, which uses mechanistic rules (interaction hypotheses) to infer directly the network structure and interaction types. Here we attempt to unravel the dark web of the plant microbiome in metabarcoding data sampled from the grapevine foliar microbiome. Using synthetic, simulated data, we first show that it is possible to satisfactorily reconstruct microbial networks using explainable machine learning. Then we confirm that the dark web of the grapevine microbiome is diverse, being composed of a range of interaction types consistent with the literature. This first attempt to use explainable machine learning to infer microbial interaction networks advances our understanding of the ecological processes that occur in microbial communities and allows us to hypothesise specific types of interaction within the grapevine microbiome. This work will have potentially valuable applications, such as the discovery of antagonistic interactions that might be used to identify potential biological control agents within the microbiome.
dc.description.sponsorshipBiosurveillance Next-Gen des changements dans la structure et le fonctionnement des écosystèmes - ANR-17-CE32-0011
dc.description.sponsorshipCultivating the grapevine without pesticides : towards agroecological wine-producing socio-ecosystems - ANR-20-PCPA-0010
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/
dc.title.enUnravelling the web of dark interactions: Explainable inference of the diversity of microbial interactions
dc.typeArticle de revue
dc.identifier.doi10.1016/bs.aecr.2023.09.005
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalAdvances in Ecological Research
bordeaux.page155-183
bordeaux.volume68
bordeaux.hal.laboratoriesBioGeCo (Biodiversité Gènes & Communautés) - UMR 1202*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04443585
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04443585v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Advances%20in%20Ecological%20Research&rft.date=2023&rft.volume=68&rft.spage=155-183&rft.epage=155-183&rft.eissn=0065-2504&rft.issn=0065-2504&rft.au=BARROSO-BERGADA,%20Didac&TAMADDONI-NEZHAD,%20Alireza&VARGHESE,%20Dany&VACHER,%20Corinne&GALIC,%20Nika&rft.genre=article


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