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dc.contributor.authorLee, Joon-Yong
dc.contributor.authorHaruta, Shin
dc.contributor.authorKato, Souichiro
dc.contributor.authorBernstein, Hans Christopher
dc.contributor.authorLindemann, Stephen R.
dc.contributor.authorLee, Dong-Yup
dc.contributor.authorFredrickson, Jim K.
dc.contributor.authorSong, Hyun-Seob
dc.date.accessioned2020-02-05T09:37:30Z
dc.date.available2020-02-05T09:37:30Z
dc.date.issued2020-01-21
dc.description.abstractModulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients – basic parameters required for implementing the MIIA – are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a <i>relative</i> sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.en_US
dc.identifier.citationLee, Haruta, Kato, Bernstein, Lindemann, Lee, Fredrickson, Song. Prediction of Neighbor-dependent Microbial Interactions from Limited Population Data. Frontiers in Microbiology. 2020en_US
dc.identifier.cristinIDFRIDAID 1790811
dc.identifier.doi10.3389/fmicb.2019.03049
dc.identifier.issn1664-302X
dc.identifier.urihttps://hdl.handle.net/10037/17327
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.relation.journalFrontiers in Microbiology
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400en_US
dc.titlePrediction of Neighbor-dependent Microbial Interactions from Limited Population Dataen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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