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Reconstruction of Danio rerio Metabolic Model Accounting for Subcellular Compartmentalisation

Overview of attention for article published in PLOS ONE, November 2012
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Title
Reconstruction of Danio rerio Metabolic Model Accounting for Subcellular Compartmentalisation
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0049903
Pubmed ID
Authors

Michaël Bekaert

Abstract

Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations (i.e., simulations of known metabolic functionalities and experimental data) strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture.

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Geographical breakdown

Country Count As %
Norway 2 5%
Netherlands 1 2%
Brazil 1 2%
United Kingdom 1 2%
Singapore 1 2%
Unknown 37 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 11 26%
Student > Master 7 16%
Student > Bachelor 3 7%
Student > Doctoral Student 2 5%
Other 4 9%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 37%
Biochemistry, Genetics and Molecular Biology 6 14%
Environmental Science 4 9%
Medicine and Dentistry 3 7%
Computer Science 2 5%
Other 5 12%
Unknown 7 16%