The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, 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, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Photosynthetic microbes associated with non-photosynthetic, heterotrophic, bacteria play a key role in the global primary production. Understanding these phototroph-heterotroph associations is therefore important, but remains challenging because they reside in chemically complex aquatic and terrestrial environments. We do not understand how the myriad of environmental parameters from nutrient availability to pH impact interactions between phototrophs and their heterotrophic partners. Here, we leverage a massively parallel droplet microfluidic platform that enables us to interrogate algae-bacteria interactions in >100,000 communities across ∼525 environmental conditions with varying pH, carbon availability and phosphorous availability. By developing a statistical framework to dissect interactions in this complex dataset, we reveal that dependance of algae-bacteria interactions on nutrient availability is strongly modulated by pH and buffering capacity. Furthermore, we show that the chemical identity of the available organic carbon source controls how pH, buffering capacity, and nutrient availability modulate algae-bacteria interactions. By leveraging a high-throughput platform, our study reveals the previously underappreciated role of pH in modulating phototroph-heterotroph interactions.