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A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology
Open accessAbstract
Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.
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“This is no longer realistic (nor necessary) in an age of post-genomics, the internet, Web 2.0 and systems biology, and when we are starting to move to integrative (if distributed) models of organisms (including humans) at a whole organ, genome or whole organism scale [118,2135,2407-2416].”
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“For example, the integration of bibliographic, metabolomic, and genomic data have been used to reconstruct the dynamic range of metabolome flow of organisms, first performed in Escherichia coli [35] and since extended to yeast [36, 37] and to individual tissues in mice [38] and humans [39].”
From Multi-omics approaches to disease (2017) · cites this paper as [37]
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Metadata
- Authors
- Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novère, N., Li, P., Liebermeister, W., Mo, M. L., Oliveira, A. P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasié, I., Weichart, D., Brent, R., Broomhead, D. S., Westerhoff, H. V., Kürdar, B., Penttilä, M., Klipp, E., Palsson, B. Ø., Sauer, U., Oliver, S. G., Mendes, P., Nielsen, J., & Kell, D. B.
- Affiliations
- University of California System (US)
- University of Manchester (GB)
- VTT Technical Research Centre of Finland (FI)
- Max Planck Institute for Molecular Genetics (DE)
- University of Zurich (CH)
- California Institute of Technology (US)
- Technical University of Denmark (DK)
- Molecular Sciences Institute (US)
- Boğaziçi University (TR)
- University of Cambridge (GB)
- Chalmers University of Technology (SE)
- Corresponding author
- Kell, Douglas B
- ORCID
- Herrgård, Markus J
- Swainston, Neil
- Dobson, Paul
- Dunn, Warwick B
- Arvas, Mikko
- Blüthgen, Nils
- Heinemann, Matthias
- Hucka, Michael
- Li, Peter
- Liebermeister, Wolfram
- Oliveira, Ana Paula
- Brent, Roger
- Westerhoff, Hans V
- Penttilä, Merja
- Klipp, Edda
- Palsson, Bernhard Ø
- Sauer, Uwe
- Oliver, Stephen G
- Mendes, Pedro
- Nielsen, Jens
- Kell, Douglas B
- Venue
- Nature Biotechnology
- Published
- 2008-10
- Volume
- 26
- Issue
- 10
- Pages
- 1155-1160
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4018421
- OpenAlex
- W2047632644
- Cited by
- 588
- References
- 69
- Citations by year
- 2026: 12 · 2025: 7 · 2024: 10 · 2023: 13 · 2022: 12 · 2021: 19 · 2020: 22 · 2019: 27 · 2018: 17 · 2017: 24 · 2016: 43 · 2015: 42 · 2014: 56 · 2013: 59 · 2012: 55
- FWCI
- 23.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- MIMS EPrints (University of Southampton) (acceptedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Chalmers Publication Library (Chalmers University of Technology) (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-sa)
- Max Planck Institute for Plasma Physics (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- VU Research Portal (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (NIH-R01)
- Directorate for Biological Sciences
- University of California, San Diego
- Engineering and Physical Sciences Research Council
- Biotechnology and Biological Sciences Research Council (BB/F010516/1, BB/C008219/1, BB/E016065/1, BB/E006248/1)
- National Institute of General Medical Sciences (R01GM071808, R01GM097479)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Saccharomyces cerevisiae, metabolic network reconstruction, consensus networks, genome-scale metabolic networks, community knowledge, chemical structure representation, SMILES, InChI, SBML
- MeSH terms
- Computer Simulation, Models, Biological, Saccharomyces cerevisiae, Signal Transduction, Information Storage and Retrieval, Systems Integration, Saccharomyces cerevisiae Proteins, Databases, Protein, Systems Biology
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