Nucleic Acids ResearchBioinformaticsPeer-reviewed journal article
Fast automated reconstruction of genome-scale metabolic models for microbial species and communities
Daniel MachadoiD, Sergej AndrejeviD, Melanie TramontanoiD, Kiran Raosaheb PatiliD
Abstract
Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism and model-guided re-engineering. Recent applications of metabolic models have also demonstrated their usefulness in unraveling cross-feeding within microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging far behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual cura-tion required to obtain good quality models and thus physiologically meaningful simulation results. Here, we present an automated tool - CarveMe - for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation-ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We build reconstructions for two model organisms, Escherichia coli and Bacillus subtillis, as well as a collection of human gut bacteria, and show that CarveMe models perform similarly to manually curated models in reproducing experimental phenotypes. Finally, we demonstrate the scalability of CarveMe through reconstructing 5587 bacterial models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.
- organism: bacteria (other)
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- approach: experimental assay
- Citations
- 748
- FWCI
- 15.29
- Field percentile
- top 1%
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