Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 438625
Title Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling
Author(s) Wodke, J.A.; Puchalka, J.; Lluch-Senar, M.; Marcos, J.; Yus, E.; Godinho, M.; Gutierrez-Gallego, R.; Martins Dos Santos, V.A.P.; Serrano, L.; Klipp, E.; Maier, T.
Source Molecular Systems Biology 9 (2013). - ISSN 1744-4292 - 19 p.
DOI https://doi.org/10.1038/msb.2013.6
Department(s) Systems and Synthetic Biology
VLAG
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) cobra toolbox extension - flux balance models - escherichia-coli - reduced bacterium - biochemical networks - invivo measurement - bacillus-subtilis - optimal selection - genetic-analysis - membrane-lipids
Abstract Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms
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