bioinfo metabolomics fluxomics

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It is a very good presentation although it is very short. Would you please send it to me (sjianxin@gmail.com) a copy? Thanks!

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Bioinformatics for Metabolomics and Fluxomics: 

Bioinformatics for Metabolomics and Fluxomics

Slide2: 

Metabolites and Metabolic Fluxes Play Key Roles in Organisms First Example Application Domain : 200,000 metabolites in plants Metabolomics: (large scale) measurements of metabolites and their levels

Slide3: 

Metabolites and Metabolic Fluxes Play Key Roles in Organisms Second Example : metabolic flux analysis in micro-organisms Fluxomics: (large scale) measurements of metabolic fluxes Metabolic flux analysis of E. coli strain grown in chemostat culture

Slide4: 

Metabolites and Metabolic Fluxes Play Key Roles in Organisms Third Example : Human and Animal Brain Neurotransmitter Cycling Fluxomics: (large scale) measurements of metabolic fluxes from: Metabolic Engineering (2004)

Goals Project: 

Goals Project develop bioinformatics methods for metabolite and pathway identification quantification of metabolite levels and isotopic composition analysis of dynamic metabolic experiments quantification of metabolic fluxes

Slide6: 

Two Connected Research Lines

Expertise in the Netherlands Bundled: 

Expertise in the Netherlands Bundled Key Participants Roeland C.H.J. van Ham, Raoul J. Bino, Centre for BioSystems Genomics / Plant Research International, Wageningen Wouter A. van Winden, Joseph J. Heijnen, Kluyver Centre / Delft University of Technology, Dept. of Biotechnology Johannes H.G.M. van Beek, Centre for Medical Systems Biology / VU University medical centre, Amsterdam Ivo H.M. van Stokkum, Centre for Medical Systems Biology / Applied Computer Science, Vrije Universiteit, Amsterdam Further participants / consultants / collaborators : on the one hand computer science/database (Bakker/Kok, Bal), signal analysis (Verheijen, Van Ormondt/De Beer) and bioinformatics (a.o. Heringa) expertise. On the other hand many scientists with metabolic research expertise and interests.

RL1: Metabolite Identification: 

RL1: Metabolite Identification Develop platform for identification of metabolites from high-throughput metabolome data algorithms for compound identification from (LC-) mass spectrometry and NMR spectroscopy databases for raw and processed information; retrieving matching spectra of known chemical composition standardized and automated procedure for metabolite identification, in particular from LC-MS/MS (liquid chromatography coupled to tandem MS)

Metabolite Identification: 

Metabolite Identification Bino et al. New Phytologist (2005) 166 : 427–438

Metabolite Identification: 

Metabolite Identification

RL2: Metabolic Flux Analysis: 

RL2: Metabolic Flux Analysis Develop platform for flux analysis, derived from stable isotope incorporation measured with NMR and mass spectrometry a problem solving environment for simulation and analysis of metabolic flux models and experimental design optimization algorithms for flux quantification new metabolic pathway modules

Slide12: 

100% 1-13C1- glucose 100% 13C2-ethanol NH4+ S. cerevisiae D=0.1 h-1 Rapid sampling of biomass 0.003 LC-MS Extraction of glycolytic, PPP, TCA intermediates from biomass m/z 13C-experiment for metabolic flux analysis in micro-organisms

Slide13: 

Detection of mass isotopomer fractions of glucose-6-phosphate with LC-MS elution time 

Slide14: 

Fit to NMR multiplets of the 4-carbon of glutamate from a biopsy from porcine heart Frequency (ppm) Flux Quantification in vivo Animal Experiment

Slide15: 

In Vivo Metabolic Rates Estimated from 13C NMR Spectrum TCA cycle flux = 7.7 ± 3.0 µmol/g/min Anaplerosis 16 ± 12 % of TCA cycle flux glutamate content 24.6 µmol/g Transport time 29.8 ± 11.6 sec 58 ± 23 % acetyl CoA from infused acetate Transamination 17.4 ± 6.0 µmol/g/min TCA cycle Flux Quantification in Vivo Animal Experiment Myocardial Metabolism

Integrated Problem Solving Environment: 

Integrated Problem Solving Environment Integrated PSE (Problem Solving Environment) for metabolic flux experiment analysis

Large Data Sets Analysed: 

Large Data Sets Analysed

Summary : 

Summary Bioinformatics tools and problem solving environ-ments are developed for metabolite identification and quantification analysis of dynamic experiments and quantification of metabolic fluxes expertise in the Netherlands is bundled collaboration of bioinformaticians, computer scientists and domain experts