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 506774
Title Metabolic network construction of the Phytophthora infestans – tomato pathosystem
Author(s) Rodenburg, Y.A.; Seidl, M.F.; Ridder, D. de; Govers, F.
Event Oomycete Molecular Genetics Network, Malmö, Sweden, 2016-06-15/2016-06-17
Department(s) Laboratory of Phytopathology
Publication type Abstract in scientific journal or proceedings
Publication year 2016
Abstract The primary and secondary metabolism of a pathogen reflects its relation with its host, as many pathogens lack essential metabolic reactions themselves, but instead exploit metabolites of their host. Therefore, reconstructing genome wide
metabolic networks for pathogens and hosts can provide new insights into their
relationship at the metabolic level. We study the interaction between the notorious plant pathogenic oomycete Phytophthora infestans and its host, tomato. Using network analyses, we aim to identify the metabolic interactions between the two species. This will provide the basis for a genome-wide model of this pathosystem at the metabolic level.
The metabolic network of P. infestans and tomato will be reconstructed based on the KEGG and MetaCyc databases. Flux balance analysis will be used to find essential reactions that characterize the metabolism of both species, and will reveal what metabolic reactions are involved by an infection. Time-series transcriptome data of the enzyme repertoire for both species will allow us to infer a dynamic representation of this pathosystem, indicating active reactions during infection stadia at different time points. Currently, methods for metabolic network modeling have been explored and draft networks have been constructed and compared. A more accurate genome sequence and annotation will provide an even higher resolution model.
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