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Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox. The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

The human gut microbiota plays a large role in the metabolism of our diet. These microorganisms can break down indigestible materials such as polysaccharides and convert them into metabolites that the human body can take up and utilize (e.g., vitamins, essential amino acids, and short-chain fatty acids). Disbalances in the gut microbiome have been associated with several diseases, including diabetes and obesity. However, little is known about the detailed metabolic crosstalk that occurs between individual organisms within the microbiome and between the microbiome and the human intestinal cells. Because of the complexity of the intestinal ecosystem, these interactions are difficult to determine using existing experimental methods. Constraint-based reconstruction and analysis (COBRA) can help identify the possible metabolic mechanisms at play in the human gut. By combining mathematical, computational, and experimental methods, we can generate hypotheses and design targeted experiments to elucidate the metabolic mechanisms in the gut microbiome. In this thesis, I first applied comparative genomics to analyze the biosynthesis pathways of eight B-vitamins in hundreds of human gut microbial species. The results suggested that many gut microbes do not synthesize any B-vitamins, that is, they depend on the host’s diet and neighboring bacteria for these essential nutrients. Second, I developed a semi-automatic reconstruction refinement pipeline that quickly generates biologically relevant genome-scale metabolic reconstructions (GENREs) of human gut microbes based on automatically generated metabolic reconstructions, comparative genomics data, and data extracted from biochemical experiments on the relevant organisms. The pipeline generated metabolically diverse reconstructions that maintain high accuracy with known biochemical data. Finally, the refined GENREs were combined with metagenomic data from individual stool samples to build personalized human gut microbiome metaboli

Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host–microbiome interactions.

The human gut microbiota supplies its host with essential nutrients, including B-vitamins. Using the PubSEED platform, we systematically assessed the genomes of 256 common human gut bacteria for the presence of biosynthesis pathways for eight B-vitamins: biotin, cobalamin, folate, niacin, pantothenate, pyridoxine, riboflavin, and thiamin. On the basis of the presence and absence of genome annotations, we predicted that each of the eight vitamins was produced by 40–65% of the 256 human gut microbes. The distribution of synthesis pathways was diverse; some genomes had all eight biosynthesis pathways, whereas others contained no de novo synthesis pathways. We compared our predictions to experimental data from 16 organisms and found 88% of our predictions to be in agreement with published data. In addition, we identified several pairs of organisms whose vitamin synthesis pathway pattern complemented those of other organisms. This analysis suggests that human gut bacteria actively exchange B-vitamins among each other, thereby enabling the survival of organisms that do not synthesize any of these essential cofactors. This result indicates the co-evolution of the gut microbes in the human gut environment. Our work presents the first comprehensive assessment of the B-vitamin synthesis capabilities of the human gut microbiota. We propose that in addition to diet, the gut microbiota is an important source of B-vitamins, and that changes in the gut microbiota composition can severely affect our dietary B-vitamin requirements.

Background: The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context. Results: We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides. Conclusions: We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.