Here we provide a broad overview of current research in modeling the growth and behavior of microbial communities, while focusing primarily on metabolic flux modeling techniques, including the reconstruction of individual species models, reconstruction of mixed-bag models, and reconstruction of multi-species models. We describe how flux balance analysis may be applied with these various model types to explore the interactions of a microbial community with its environment, as well as the interactions of individual species with each other. We demonstrate all discussed model reconstruction and analysis approaches using the Department of Energy’s Systems Biology Knowledgebase (KBase), constructing and importing genome-scale metabolic models of Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii, and subsequently combining them into a community model of the gut microbiome. We also use KBase to explore how these species interact with each other and with the gut environment, exploring the trade-offs in information provided by applying each metabolic flux modeling approach. Overall, we conclude that no single approach is better than the others, and often there is much to be gained by applying multiple approaches synergistically when exploring the ecology of a microbial community.
Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe's entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Python-based open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe's metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.