The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

PSAMM is an open source software package that supports the iterative curation and analysis of genome-scale models (GEMs). It aims to integrate the annotation and consistency checking of metabolic models with the simulation of metabolic fluxes. The model representation in PSAMM is compatible with version tracking systems like Git, which allows for full documentation of model file changes and enables collaborative curations of large, complex models. This chapter provides a protocol for using PSAMM functions and a detailed description of the various aspects in setting up and using PSAMM for the simulation and analysis of metabolic models. The overall PSAMM workflow outlined in this chapter includes the import and export of model files, the documentation of model modifications using the Git version control system, the application of consistency checking functions for model curations, and the numerical simulation of metabolic models.

A novel in vitro gut model was developed to better understand the interactions between Escherichia coli and the mouse cecal mucus commensal microbiota. The gut model is simple and inexpensive while providing an environment that largely replicates the nonadherent mucus layer of the mouse cecum. 16S rRNA gene profiling of the cecal microbial communities of streptomycin-treated mice colonized with E. coli MG1655 or E. coli Nissle 1917 and the gut model confirmed that the gut model properly reflected the community structure of the mouse intestine. Furthermore, the results from the in vitro gut model mimic the results of published in vivo competitive colonization experiments. The gut model is initiated by the colonization of streptomycin-treated mice, and then the community is serially transferred in microcentrifuge tubes in an anaerobic environment generated in anaerobe jars. The nutritional makeup of the cecum is simulated in the gut model by using a medium consisting of porcine mucin, mouse cecal mucus, HEPES-Hanks buffer (pH 7.2), Cleland's reagent, and agarose. Agarose was found to be essential for maintaining the stability of the microbial community in the gut model. The outcome of competitions between E. coli strains in the in vitro gut model is readily explained by the "restaurant hypothesis" of intestinal colonization. This simple model system potentially can be used to more fully understand how different members of the microbiota interact physically and metabolically during the colonization of the intestinal mucus layer. IMPORTANCE Both commensal and pathogenic strains of Escherichia coli appear to colonize the mammalian intestine by interacting physically and metabolically with other members of the microbiota in the mucus layer that overlays the cecal and colonic epithelium. However, the use of animal models and the complexity of the mammalian gut make it difficult to isolate experimental variables that might dictate the interactions between E. coli and other members of the microbiota, such as those that are critical for successful colonization. Here, we describe a simple and relatively inexpensive in vitro gut model that largely mimics in vivo conditions and therefore can facilitate the manipulation of experimental variables for studying the interactions of E. coli with the intestinal microbiota.

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).