Literature DB >> 34635859

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

Ilija Dukovski1,2, Djordje Bajić3,4, Jeremy M Chacón5,6, Michael Quintin1,2, Jean C C Vila3,4, Snorre Sulheim1,7,8, Alan R Pacheco1,2, David B Bernstein2,9, William J Riehl10, Kirill S Korolev1,2,11, Alvaro Sanchez3,4, William R Harcombe5,6, Daniel Segrè12,13,14,15,16.   

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.
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.

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Year:  2021        PMID: 34635859     DOI: 10.1038/s41596-021-00593-3

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  93 in total

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Journal:  Science       Date:  2015-12-11       Impact factor: 47.728

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Authors:  Wenying Shou; Sri Ram; Jose M G Vilar
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-31       Impact factor: 11.205

3.  Massively parallel screening of synthetic microbial communities.

Authors:  Jared Kehe; Anthony Kulesa; Anthony Ortiz; Cheri M Ackerman; Sri Gowtham Thakku; Daniel Sellers; Seppe Kuehn; Jeff Gore; Jonathan Friedman; Paul C Blainey
Journal:  Proc Natl Acad Sci U S A       Date:  2019-06-11       Impact factor: 11.205

Review 4.  Establishing Causality: Opportunities of Synthetic Communities for Plant Microbiome Research.

Authors:  Julia A Vorholt; Christine Vogel; Charlotte I Carlström; Daniel B Müller
Journal:  Cell Host Microbe       Date:  2017-08-09       Impact factor: 21.023

Review 5.  Microbial communities associated with plants: learning from nature to apply it in agriculture.

Authors:  Fernando Dini Andreote; Michele de Cássia Pereira E Silva
Journal:  Curr Opin Microbiol       Date:  2017-04-22       Impact factor: 7.934

Review 6.  Principles for designing synthetic microbial communities.

Authors:  Nathan I Johns; Tomasz Blazejewski; Antonio Lc Gomes; Harris H Wang
Journal:  Curr Opin Microbiol       Date:  2016-04-13       Impact factor: 7.934

Review 7.  Synthetic microbial communities.

Authors:  Tobias Grosskopf; Orkun S Soyer
Journal:  Curr Opin Microbiol       Date:  2014-03-14       Impact factor: 7.934

8.  A communal catalogue reveals Earth's multiscale microbial diversity.

Authors:  Luke R Thompson; Jon G Sanders; Daniel McDonald; Amnon Amir; Joshua Ladau; Kenneth J Locey; Robert J Prill; Anupriya Tripathi; Sean M Gibbons; Gail Ackermann; Jose A Navas-Molina; Stefan Janssen; Evguenia Kopylova; Yoshiki Vázquez-Baeza; Antonio González; James T Morton; Siavash Mirarab; Zhenjiang Zech Xu; Lingjing Jiang; Mohamed F Haroon; Jad Kanbar; Qiyun Zhu; Se Jin Song; Tomasz Kosciolek; Nicholas A Bokulich; Joshua Lefler; Colin J Brislawn; Gregory Humphrey; Sarah M Owens; Jarrad Hampton-Marcell; Donna Berg-Lyons; Valerie McKenzie; Noah Fierer; Jed A Fuhrman; Aaron Clauset; Rick L Stevens; Ashley Shade; Katherine S Pollard; Kelly D Goodwin; Janet K Jansson; Jack A Gilbert; Rob Knight
Journal:  Nature       Date:  2017-11-01       Impact factor: 49.962

9.  The evolution of the host microbiome as an ecosystem on a leash.

Authors:  Kevin R Foster; Jonas Schluter; Katharine Z Coyte; Seth Rakoff-Nahoum
Journal:  Nature       Date:  2017-08-02       Impact factor: 49.962

10.  Deciphering microbial interactions in synthetic human gut microbiome communities.

Authors:  Ophelia S Venturelli; Alex C Carr; Garth Fisher; Ryan H Hsu; Rebecca Lau; Benjamin P Bowen; Susan Hromada; Trent Northen; Adam P Arkin
Journal:  Mol Syst Biol       Date:  2018-06-21       Impact factor: 11.429

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  12 in total

Review 1.  Microbiome engineering for bioremediation of emerging pollutants.

Authors:  L Paikhomba Singha; Pratyoosh Shukla
Journal:  Bioprocess Biosyst Eng       Date:  2022-08-27       Impact factor: 3.434

Review 2.  Ecological modelling approaches for predicting emergent properties in microbial communities.

Authors:  Naomi Iris van den Berg; Daniel Machado; Sophia Santos; Isabel Rocha; Jeremy Chacón; William Harcombe; Sara Mitri; Kiran R Patil
Journal:  Nat Ecol Evol       Date:  2022-05-16       Impact factor: 19.100

3.  Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia.

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Journal:  R Soc Open Sci       Date:  2022-05-04       Impact factor: 3.653

4.  Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth.

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Journal:  Front Microbiol       Date:  2022-05-17       Impact factor: 6.064

5.  CAMDLES: CFD-DEM Simulation of Microbial Communities in Spaceflight and Artificial Microgravity.

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Journal:  Life (Basel)       Date:  2022-04-29

6.  Enhancing Microbiome Research through Genome-Scale Metabolic Modeling.

Authors:  Nana Y D Ankrah; David B Bernstein; Matthew Biggs; Maureen Carey; Melinda Engevik; Beatriz García-Jiménez; Meiyappan Lakshmanan; Alan R Pacheco; Snorre Sulheim; Gregory L Medlock
Journal:  mSystems       Date:  2021-12-14       Impact factor: 6.496

Review 7.  Modeling approaches for probing cross-feeding interactions in the human gut microbiome.

Authors:  Pedro Saa; Arles Urrutia; Claudia Silva-Andrade; Alberto J Martín; Daniel Garrido
Journal:  Comput Struct Biotechnol J       Date:  2021-12-08       Impact factor: 7.271

8.  Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil.

Authors:  Kusum Dhakar; Raphy Zarecki; Shlomit Medina; Hamam Ziadna; Karam Igbaria; Ran Lati; Zeev Ronen; Hanan Eizenberg; Shiri Freilich
Journal:  mSystems       Date:  2022-08-01       Impact factor: 7.324

9.  A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast.

Authors:  Pranas Grigaitis; Douwe A J Grundel; Eunice van Pelt-KleinJan; Mirushe Isaku; Guixiang Xie; Sebastian Mendoza Farias; Bas Teusink; Johan H van Heerden
Journal:  mSystems       Date:  2022-08-11       Impact factor: 7.324

10.  A gap-filling algorithm for prediction of metabolic interactions in microbial communities.

Authors:  Dafni Giannari; Cleo Hanchen Ho; Radhakrishnan Mahadevan
Journal:  PLoS Comput Biol       Date:  2021-11-01       Impact factor: 4.475

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