Abstract
Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 5030-5082 |
| Number of pages | 53 |
| Journal | Nature Protocols |
| Volume | 16 |
| Issue number | 11 |
| Early online date | Oct 11 2021 |
| DOIs | |
| State | Published - Nov 2021 |
Bibliographical note
Funding Information:We are grateful to members of the Segrè, Sanchez and Harcombe labs for helpful inputs and discussions at multiple stages of the development of COMETS. We also thank M. Hasson for his contribution to the development of the code. The development of COMETS was initially supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research, grant DESC0004962 to D.S. D.S. also acknowledges funding from the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research through the Microbial Community Analysis and Functional Evaluation in Soils SFA Program (m-CAFEs) under contract number DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory; the NIH (T32GM100842, 5R01DE024468, R01GM121950), the National Science Foundation (1457695 and NSFOCE-BSF 1635070), the Human Frontiers Science Program (RGP0020/2016) and the Boston University Interdisciplinary Biomedical Research Office. A.R.P. was supported by a Howard Hughes Medical Institute Gilliam Fellowship and a National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship. S.S. was funded by SINTEF, the Norwegian graduate research school in bioinformatics, biostatistics and systems biology (NORBIS) and by the INBioPharm project of the Centre for Digital Life Norway (Research Council of Norway grant no. 248885). W.R.H. acknowledges funding from RO1GM121498. Work by A.S., D.B. and J.C.C.V. was supported by a young investigator award from the Human Frontier Science Program (RGY0077/2016), by a Packard Fellowship from the David and Lucile Packard foundation, and by the National Institutes of Health through grant 1R35 GM133467-01 to A.S. K.S.K. was supported by Simons Foundation Grants #409704 and by the Research Corporation for Science Advancement through Cottrell Scholar Award #24010.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.