Evidence map›Paper›PMID 20057383›Full record

ArticleNature protocols2010

A protocol for generating a high-quality genome-scale metabolic reconstruction.

Ines Thiele, Bernhard Ø Palsson

Abstract read
In one paragraph

Article in Nature protocols, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 870 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
870citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

870 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Environmental formermSystems · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Nutrient control enables metabolic reconstruction ofbioRxiv : the preprint server for biology · 2026
    Article
  11. Article
  12. Article
  13. Article
  14. Review
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  16. Article
  17. Multi-strain analysis ofmSystems · 2026
    Article
  18. Review
  19. Review
  20. Review

810 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ines ThieleDepartment of Bioengineering, University of California, San Diego, La Jolla, California, USA.
Bernhard Ø Palsson

Funding

GENOMICALLY BASED MODELS FOR ANTIBIOTIC DEVELOPMENTR01GM057089 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALSSON, BERNHARD O · 1998 to 2021
$6.9M
NIGMS NIH HHS R01 GM057089
6 · The paper itself

Abstract

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

Indexed as

BiomassComputer SimulationGene Expression ProfilingGene Expression RegulationGenomeMetabolomeSoftware

Identifiers

PMID20057383
PMCPMC3125167

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.