Literature DB >> 21943912

A practical guide to genome-scale metabolic models and their analysis.

Filipe Santos1, Joost Boele, Bas Teusink.   

Abstract

Genome-scale metabolic reconstructions and their analysis with constraint-based modeling techniques have gained enormous momentum. It is a natural next step after sequencing of a genome, as a technique that links top-down systems biology analyses at genome scale with bottom-up systems biology modeling scrutiny. This chapter aims at (systems) biologists that have an interest in, but no extensive knowledge of, applying genome-scale metabolic reconstruction and modeling to their organism. Rather than being comprehensive--excellent and extensive reviews exist on every aspect of this field--we give a rather personal account on our experience with the process of reconstruction and modeling. First, we place genome-scale metabolic models in the spectrum of modeling approaches, and rather extensively discuss, for nonexperts, the central concept in constraint-based modeling: the solution space that is bounded through constraints on fluxes. We subsequently provide an overview of the different steps involved in metabolic reconstruction and modeling, pointing to aspects that we found difficult, important, not well enough addressed in the current reviews, or any combination thereof. In this way, we hope that this chapter serves as a practical guide through the field.
Copyright © 2011 Elsevier Inc. All rights reserved.

Mesh:

Year:  2011        PMID: 21943912     DOI: 10.1016/B978-0-12-385118-5.00024-4

Source DB:  PubMed          Journal:  Methods Enzymol        ISSN: 0076-6879            Impact factor:   1.600


  15 in total

1.  Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice.

Authors:  Bin Du; Daniel C Zielinski; Jonathan M Monk; Bernhard O Palsson
Journal:  Proc Natl Acad Sci U S A       Date:  2018-10-11       Impact factor: 11.205

2.  A data integration and visualization resource for the metabolic network of Synechocystis sp. PCC 6803.

Authors:  Timo R Maarleveld; Joost Boele; Frank J Bruggeman; Bas Teusink
Journal:  Plant Physiol       Date:  2014-01-08       Impact factor: 8.340

3.  Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum.

Authors:  Sara Benito-Vaquerizo; Ivette Parera Olm; Thijs de Vroet; Peter J Schaap; Diana Z Sousa; Vitor A P Martins Dos Santos; Maria Suarez-Diez
Journal:  Microb Cell Fact       Date:  2022-06-16       Impact factor: 6.352

Review 4.  Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

Authors:  Michael A Henson
Journal:  Biochem Soc Trans       Date:  2015-12       Impact factor: 5.407

Review 5.  Dynamic flux balance analysis for synthetic microbial communities.

Authors:  Michael A Henson; Timothy J Hanly
Journal:  IET Syst Biol       Date:  2014-10       Impact factor: 1.615

Review 6.  Metabolic shifts: a fitness perspective for microbial cell factories.

Authors:  Anisha Goel; Meike Tessa Wortel; Douwe Molenaar; Bas Teusink
Journal:  Biotechnol Lett       Date:  2012-08-31       Impact factor: 2.461

7.  Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks.

Authors:  Steven M Kelk; Brett G Olivier; Leen Stougie; Frank J Bruggeman
Journal:  Sci Rep       Date:  2012-08-15       Impact factor: 4.379

8.  Community flux balance analysis for microbial consortia at balanced growth.

Authors:  Ruchir A Khandelwal; Brett G Olivier; Wilfred F M Röling; Bas Teusink; Frank J Bruggeman
Journal:  PLoS One       Date:  2013-05-31       Impact factor: 3.240

9.  Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of Blattabacterium cuenoti.

Authors:  Miguel Ponce-de-León; Francisco Montero; Juli Peretó
Journal:  BMC Syst Biol       Date:  2013-10-31

10.  Modeling of Zymomonas mobilis central metabolism for novel metabolic engineering strategies.

Authors:  Uldis Kalnenieks; Agris Pentjuss; Reinis Rutkis; Egils Stalidzans; David A Fell
Journal:  Front Microbiol       Date:  2014-02-05       Impact factor: 5.640

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