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

Abstract

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 essential resources 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 get a glimpse of 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

Ecological landscapes guide the assembly of optimal microbial communities

“…Here, we study the success of the heuristic search by simulating a range of realistic and widelyused consumer-resource models of microbial communities, which have reproduced many features of natural and experimental communities [22][23][24]. Compared to the commonly-used Lotka-Volterra models, our approach is more realistic and makes a closer connection to the metabolic interactions that underpin many optimization problems in biotechnology [25][26][27][28][29]. Furthermore, it is much easier to make educated guesses about the production and consumption of resources than interaction coefficients.…”