Literature DB >> 25725218

Critical assessment of genome-scale metabolic networks: the need for a unified standard.

Aarthi Ravikrishnan, Karthik Raman.   

Abstract

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.
© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Keywords:  SBML; blocked reactions; genome-scale metabolic networks; reconstruction; standards

Mesh:

Year:  2015        PMID: 25725218     DOI: 10.1093/bib/bbv003

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  26 in total

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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

4.  Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum.

Authors:  Abhishek Subramanian; Ram Rup Sarkar
Journal:  Sci Rep       Date:  2017-08-31       Impact factor: 4.379

5.  Enumerating all possible biosynthetic pathways in metabolic networks.

Authors:  Aarthi Ravikrishnan; Meghana Nasre; Karthik Raman
Journal:  Sci Rep       Date:  2018-07-02       Impact factor: 4.379

6.  Do genome-scale models need exact solvers or clearer standards?

Authors:  Ali Ebrahim; Eivind Almaas; Eugen Bauer; Aarash Bordbar; Anthony P Burgard; Roger L Chang; Andreas Dräger; Iman Famili; Adam M Feist; Ronan Mt Fleming; Stephen S Fong; Vassily Hatzimanikatis; Markus J Herrgård; Allen Holder; Michael Hucka; Daniel Hyduke; Neema Jamshidi; Sang Yup Lee; Nicolas Le Novère; Joshua A Lerman; Nathan E Lewis; Ding Ma; Radhakrishnan Mahadevan; Costas Maranas; Harish Nagarajan; Ali Navid; Jens Nielsen; Lars K Nielsen; Juan Nogales; Alberto Noronha; Csaba Pal; Bernhard O Palsson; Jason A Papin; Kiran R Patil; Nathan D Price; Jennifer L Reed; Michael Saunders; Ryan S Senger; Nikolaus Sonnenschein; Yuekai Sun; Ines Thiele
Journal:  Mol Syst Biol       Date:  2015-10-14       Impact factor: 11.429

Review 7.  Systems biology of host-microbe metabolomics.

Authors:  Almut Heinken; Ines Thiele
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2015-04-30

8.  PSAMM: A Portable System for the Analysis of Metabolic Models.

Authors:  Jon Lund Steffensen; Keith Dufault-Thompson; Ying Zhang
Journal:  PLoS Comput Biol       Date:  2016-02-01       Impact factor: 4.475

Review 9.  Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine.

Authors:  Cheng Zhang; Qiang Hua
Journal:  Front Physiol       Date:  2016-01-07       Impact factor: 4.566

Review 10.  Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells.

Authors:  Partho Sen; Esko Kemppainen; Matej Orešič
Journal:  Front Mol Biosci       Date:  2018-01-09
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