A promising Computational pipeline for microbial consortia analysis using Genome-scale metabolic models

The increasing interest of complex microbial communities for biotechnological solutions requires the development of computational workflows assisting the analysis of these communities. Due to the lack of tools to achieve this goal, here, we present a reproducible bioinformatic pipeline for the functional potential of natural microbial communities and the description of their interspecific interactions using genome-scale metabolic models (GEMs). The analysis starts from metagenomic data like 16s rRNA amplicon sequencing or whole metagenomic sequencing. Using the integrated, and well established tools, for taxonomic identification like Ampliseq or taxprofiler, genomic data can be retrieved and the metabolic functional information are inferred. The last step involves the translation of this information into GEMs, one of the major modeling approaches for systems-level metabolic studies to infer the interactions and allow the phenotype predictions. The result is the creation of in silico microbial consortia with modeled members and interactions. This promising analysis approach allows the study of an enriched cyanobacterial community with the creation of in silico microbial consortia giving promising results for optimization of a microbial consortium for biotechnological production of Polyhydroxybutyrate (PHB).