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First NMC paper published

In a joint NMC project between the group of metabolic diseases at UMC Utrecht (Margriet Hendriks) and the Biosystems Data Analysis group of the UvA, the NMC postdoc, Tunahan Cakir, has investigated how to infer metabolic networks from metabolomics data. This research is reported in the Metabolomics journal and is the first scientific Netherlands Metabolomis Centre (NMC) publication:

T. Cakir, M.M.W.B. Hendriks, J.A. Westerhuis and A.K. Smilde; Metabolic network discovery through reverse engineering of metabolome data, Metabolomics, DOI 10.1007/s11306-009—156-4, January 2009.

In an extensive set of simulations (see Figure) different types of perturbations were investigated and combined with different network inference tools. The results showed clearly that some kind of pruning is necessary and the design of the perturbation is important.

The approach followed for metabolic network inference. Three data sets with different variability properties are collected in silico. Each data set is processed to calculate similarity scores with linear and nonlinear methods (relevance networks). Alternative scores which remove indirect interactions are also applied (Conditioned networks). All these networks are fed into a pruning algorithm.


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First NMC paper published In a joint NMC project between the group of metabolic diseases at UMC Utrecht (Margriet Hendriks) and the Biosystems Data Analysis group of the UvA, the NMC postdoc, Tunahan Cakir, has investigated how to infer metabolic networks from metabolomics data. This research is reported in the Metabolomics journal and is the first scientific Netherlands Metabolomis Centre (NMC) publication: T. Cakir, M.M.W.B. Hendriks, J.A. Westerhuis and A.K. Smilde; Metabolic network discovery through reverse engineering of metabolome data, Metabolomics, DOI 10.1007/s11306-009—156-4, January 2009. In an extensive set of simulations (see Figure) different types of perturbations were investigated and combined with different network inference tools. The results showed clearly that some kind of pruning is necessary and the design of the perturbation is important. The approach followed for metabolic network inference. Three data sets with different variability properties are collected in silico. Each data set is processed to calculate similarity scores with linear and nonlinear methods (relevance networks). Alternative scores which remove indirect interactions are also applied (Conditioned networks). All these networks are fed into a pruning algorithm.		Life Sciences Momentum NGI The Hague, 23 November 2010 Johan Westerhuis receives EAS Award Amsterdam, March 2010 30th ISPPP - Call for Papers Bologna, Italy, 6-8 September 2010 NGI sponsors Postdoc Retreat The Hague, 21-23 April 2010 Pre-Seed Grant NGI The Hague, January 2010 Metabolomics in Nature's vision 2020 Nature Magazine 7 January 2010 Metabolomics receives first ever Impact Factor Rating UK 17 August 2009 NMC makes the difference NMC, Leiden Puzzling with metabolites NMC, NBIC, May 2009 First NMC paper published Amsterdam, Utrecht, January 2009 Robert Hall elected as secretary Metabolomics Society 11 December 2008

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