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• W2652002008 abstract "Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These “super-organisms” play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g. metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as Flux Balance Analysis (FBA), has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in" @default.
• W2652002008 created "2017-06-30" @default.
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• W2652002008 date "2017-06-21" @default.
• W2652002008 modified "2023-10-16" @default.
• W2652002008 title "Perspectives and Challenges in Microbial Communities Metabolic Modeling" @default.
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• W2652002008 doi "https://doi.org/10.3389/fgene.2017.00088" @default.
• W2652002008 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5478693" @default.
• W2652002008 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28680442" @default.
• W2652002008 hasPublicationYear "2017" @default.
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