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A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)

Nature Protocols volume 16, pages 5030–5082 (2021)Cite this article

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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.

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Fig. 1: Overview of the COMETS platform.
Fig. 2: Basic workflows.
Fig. 3: Growth of E. coli (core model) batch culture in minimal medium, with glucose as the only carbon source.
Fig. 4: Chemostat simulation of cross-feeding E. coli strains grown on lactose.
Fig. 5: Simulations of the diurnal cycle of the marine photoautotrophic bacteria Prochlorococcus.
Fig. 6: Media concentrations over time during simulations demonstrating extracellular reactions.
Fig. 7: Simulation of evolutionary processes.
Fig. 8: Soil–air interface simulation.
Fig. 9: A variety of morphologies simulated by COMETS.

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Data availability

The COMETS Protocols GitHub repository (https://github.com/segrelab/COMETS_Protocols) contains all input files and jupyter notebooks from which one can reproduce the results presented in this protocol. The data are distributed under the Creative Commons CC0 1.0 Universal license.

Code availability

COMETS (https://www.runcomets.org) is an open-source code, and it is available at https://github.com/segrelab/comets. The code is distributed under the GNU General Public License Version 3. The documentation is available at https://segrelab.github.io/comets-manual/, which is structured as a tutorial and contains additional examples not shown in this protocol. The MATLAB toolbox is available at https://github.com/segrelab/comets-toolbox, distributed under the GNU General Public License Version 3. The COMETS Python toolbox is available at https://github.com/segrelab/cometspy, distributed under the GNU General Public License Version 3. The code in this protocol has been peer-reviewed.

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Acknowledgements

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 DE-SC0004962 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.

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Author notes

  1. These authors contributed equally: Ilija Dukovski, Djordje Bajić, Jeremy M Chacón, Michael Quintin.

Authors and Affiliations

  1. Bioinformatics Program, Boston University, Boston, MA, USA

    Ilija Dukovski, Michael Quintin, Snorre Sulheim, Alan R. Pacheco, Kirill S. Korolev & Daniel Segrè

  2. Biological Design Center, Boston University, Boston, MA, USA

    Ilija Dukovski, Michael Quintin, Alan R. Pacheco, David B. Bernstein, Kirill S. Korolev & Daniel Segrè

  3. Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA

    Djordje Bajić, Jean C. C. Vila & Alvaro Sanchez

  4. Microbial Sciences Institute, Yale University, West Haven, CT, USA

    Djordje Bajić, Jean C. C. Vila & Alvaro Sanchez

  5. Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA

    Jeremy M. Chacón & William R. Harcombe

  6. BioTechnology Institute, University of Minnesota, St. Paul, MN, USA

    Jeremy M. Chacón & William R. Harcombe

  7. Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway

    Snorre Sulheim

  8. Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway

    Snorre Sulheim

  9. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    David B. Bernstein & Daniel Segrè

  10. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    William J. Riehl

  11. Department of Physics, Boston University, Boston, MA, USA

    Kirill S. Korolev & Daniel Segrè

  12. Department of Biology, Boston University, Boston, MA, USA

    Daniel Segrè

Authors
  1. Ilija Dukovski
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  4. Michael Quintin
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  5. Jean C. C. Vila
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Contributions

Overall management of COMETS platform: D.S., I.D. Conceptualization of COMETS capabilities: D.S., I.D., A.S., W.H., K.K. Writing and maintenance of initial COMETS code: I.D., W.J.R. Development of current COMETS software and capabilities: I.D., D.B., J.C., M.Q. Writing of specific modules: W.J.R., I.D., J.C., D.B., M.Q., S.S. Preparing and implementing protocols: I.D., J.C., D.B., M.Q., A.R.P., S.S., J.V. Conceptualization and preparation of the manuscript: I.D., D.B., J.C., M.Q., J.V., S.S., A.R.P., D.B.B., K.K., A.S., W.H., D.S.

Corresponding author

Correspondence to Daniel Segrè.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Harcombe, W. R. et al. Cell Rep. 7, 1104–1115 (2104): https://doi.org/10.1016/j.celrep.2014.03.070

Chacón, J. M., Möbius, W. & Harcombe, W. R. ISME J. 12, 669–680 (2018): https://doi.org/10.1038/s41396-017-0038-0

Bajić, D. et al. Proc. Natl Acad. Sci. USA 115, 11286–11291 (2018): https://doi.org/10.1073/pnas.1808485115

Extended data

Extended Data Fig. 1 Sensitivity of the simulation results depending on the value of the finite time step.

Starting with a simulation identical to the one in Procedure 7, we repeated it with four different values of the time step: a, images of the final colony morphologies; b, plot of the total biomass change with time, illustrating the magnitude of the error due to the finite time step size. The simulation time step size should be chosen such that final simulation result is within the tolerated error.

Extended Data Fig. 2 Sensitivity of the simulation results depending on the value of the finite spatial grid size.

Starting with a simulation identical to the one in Procedure 7, we repeated it with four different values of the grid size: a, images of the final colony morphologies; b, plot of the total biomass change with time, illustrating the magnitude of the error due to the finite grid size. The simulation finite spatial grid size should be chosen such that the final simulation result is within the tolerated error.

Extended Data Fig. 3 Sensitivity of the simulation results depending on the value of the amplitude of the demographic noise.

Starting with a simulation identical to the one in Procedure 7, we repeated it with two different magnitudes of the noise amplitude σ: a,b, images of three replicas of a colony simulation (a), and plot of the total biomass change with time of the three replicas simulations with σ = 0.01 (b); c,d, Images of three replicas of a colony simulation (c), and plot of the total biomass change with time of the three replicas simulations with σ = 0.001 (d). A finalized result of a simulation study in presence of noise should be averaged over several replicas of the stochastic simulation. The change of the noise amplitude, however, may have a substantial effect of the growth rate and the final morphology. The value of the noise amplitude should be chosen to best represent an experimental result.

Extended Data Fig. 4 COMETS simulations time benchmarking.

In order to benchmark the performance of COMETS with increasing complexity of the simulated system, we performed a 24 h batch culture run similar to that in Procedure 1, with either 1, 10 or 100 models (the E. coli model iJO1366 was used in all instances). The settings were identical to Procedure 1 in the main text. We tested three timesteps, 0.01 h (circles), 0.1 h (triangles) and 0.5 h. (squares). The x-axis shows simulated time (i.e., number of simulation steps × timeStep, in h); the y-axis shows elapsed simulation time (the time taken by the computer to run the program), in min. Simulations were performed in Python using cometspy in a personal laptop running linux (Intel Core i7-10610U CPU at 1.80 GHz × 4 cores, 15.3 GiB memory).

Extended Data Fig. 5 The GUI of COMETS.

COMETS simulations can be started from the GUI by loading a previously prepared layout, models and parameters files. It is meant mostly as a training tool with limited functionality. Future development of COMETS will focus on the development of a comprehensive GUI.

Supplementary information

Supplementary Information

Supplementary Discussions 1–5, Supplementary Table 1 and References.

Reporting Summary

Extended Data Video 1

Branching colony of two identical strains of E. coli. Procedure 7, option A: growth regime without genetic demixing.

Extended Data Video 2

Branching colony of two identical strains of E. coli. Procedure 7, option B: growth regime with genetic demixing.

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Dukovski, I., Bajić, D., Chacón, J.M. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat Protoc 16, 5030–5082 (2021). https://doi.org/10.1038/s41596-021-00593-3

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