A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities. (August 2020) - Antoniewicz, Maciek R
Graphical Abstract: Highlights: New predictive modeling approaches and better tools to manipulate microbiome interactions are needed. Identifying and validating cross–feeding interactions in microbial consortia is still a challenging problem. 13 C-metabolic flux analysis methods have been developed that can measure fluxes and cross-feeding interactions in consortia. Microbial community models that span different length scales and time scales enable integrated analysis of multi-omics data. Abstract : Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions.