Evidence map›Paper›PMID 18974823›Full record

ArticlePLoS computational biology2008

Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology.

Jacek Puchałka, Matthew A Oberhardt, Miguel Godinho, Agata Bielecka, Daniela Regenhardt, Kenneth N Timmis, Jason A Papin, Vítor A P Martins dos Santos

Abstract read
In one paragraph

Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed
–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

111 citing papers in PubMed.

  1. Multi-strain analysis ofmSystems · 2026
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  2. Review
  3. Predicting Strain-Specific Metabolic Capabilities in the GenusComputational and structural biotechnology journal · 2026
    Article
  4. Review
  5. Multi-strain Analysis ofbioRxiv : the preprint server for biology · 2025
    Article
  6. Review
  7. Article
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  20. Review

51 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

8 authors.

Jacek PuchałkaSynthetic and Systems Biology Group, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany.
Matthew A Oberhardt
Miguel Godinho
Agata Bielecka
Daniela Regenhardt
Kenneth N Timmis
Jason A Papin
Vítor A P Martins dos Santos

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential.

Indexed as

Models, BiologicalBiocatalysisBiomassBiotechnologyDatabases, GeneticGenome, BacterialGenomicsMetabolic Networks and PathwaysMutagenesis, Site-DirectedPolyhydroxyalkanoatesPseudomonas putidaSystems BiologyPolyhydroxyalkanoates

Identifiers

PMID18974823
PMCPMC2563689

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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.