An ensemble approach to the structure-function problem in microbial communities

• Published in: arXiv.org
• Main Authors: Gopalakrishnappa, Chandana, Gowda, Karna, Prabhakara, Kaumudi, Kuehn, Seppe
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.11.2021
• Subjects: Biomass energy production; Biosphere; Constituents; Data analysis; Metabolism; Metabolites; Microorganisms; Quantitative Biology - Populations and Evolution
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2111.06279

The metabolic activity of microbes has played an essential role in the evolution and persistence of life on Earth. Microbial metabolism plays a primary role in the flow of carbon, nitrogen and other elements through the biosphere on a global scale. Microbes perform these metabolic activities in the context of complex communities comprised of many species that interact in dynamic and spatially-structured environments. Molecular genetics has revealed many of the metabolic pathways microbes utilize to generate energy and biomass. However, most of this knowledge is derived from model organisms, so we have a limited view of role of the massive genomic diversity in the wild on metabolic phenotypes. Further, we are only beginning to glimpse the principles governing how the metabolism of a community emerges from the collective action of its constituent members. As a result, one of the biggest challenges in the field is to understand how the metabolic activity of a community emerges from the genomic structure of the constituents. Here we propose an approach to this problem that rests on the quantitative analysis of metabolic activity in ensembles of microbial communities. We propose quantifying metabolic fluxes in diverse communities, either in the laboratory or the wild. We suggest that using sequencing data to quantify the genomic, taxonomic or transcriptional variation across an ensemble of communities can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited and then review the analytical techniques available for quantifying metabolite dynamics in communities. Finally, we discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.