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Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing
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Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing
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Journal Article
Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing
2024
Overview
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome‐scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta‐)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third‐generation sequencing, and we discuss the opportunities of long‐read sequencing, strain‐level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling. With this review we articulate two important topics in the context of deciphering the role and functions of microbial communities: metabolic modeling and metagenomics. We survey the methodological approaches, highlight the changes raised by third generation sequencing, and provide resources to bridge the gaps between the sequencing reads and the models.
