N2BPC: an algorithmic approach from Networks to Bacteria's metabolite Production and Consumption

Microorganisms tend to coexist and form complex relationships between themselves and the environment they inhabit. The development of increasingly efficient and cost-effective high-throughput 16S rDNA-seq techniques has enhanced the possibility of studying the networks of interactions and understanding the drivers of a bacteria community organization. Many network inference methods have been developed to answer this open question; however, the lack of a known biological truth makes it difficult to validate the results obtained. Therefore, in silico solutions are needed to simulate realistic gold standards. In this work, we propose N2BPC, an algorithmic approach that, starting from a known weighted and directed network topology assumed to be the ground truth for microbial interactions, generate the consumer preferences (C) and metabolic rules (D) of a Microbial Consumer Resource Model (MiCRM) for simulating bacteria community. N2BPC models microbial interactions as metabolites that are consumed by the target nodes of every edge, and ensure the uniqueness of the generated C and D.