Puchalka2008 - Genome-scale metabolic network of Pseudomonas putida (iJP815)

Puchalka2008 - Genome-scale metabolic network
of Pseudomonas putida (iJP815)

This model is described in the article:

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

Puchałka J, Oberhardt MA,
Godinho M, Bielecka A, Regenhardt D, Timmis KN, Papin JA, Martins
dos Santos VA.
PLoS Comput. Biol. 2008 Oct; 4(10):
e1000210
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.

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