Max-Planck-Institut für Informatik
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Analyzing metabolic networks in yeast

This project aims at the in-depth metabolic investigation of the eukaryotic model organism S. cerevisiae. We are applying bioinformatics methods and statistical analysis techniques in combination with stoichiometric and flux profiling to understand the function of specific genes involved in central carbon metabolism and amino acid biosynthesis, and their regulatory mechanisms as well as to study environmental influences, e.g. application of drugs, on cellular metabolism [1]. For this purpose we screen mutants from a knock-out library of S. cerevisiae for correlations between gene deletion and metabolic physiology. Detailed and quantitative information on the corresponding physiological states is obtained by 13C-metabolic flux analysis of cultures grown in novel oxygen sensor microtiter plates. Bioinformatics methods are developed and applied to select appropriate knock-out strains, design experiments, automatically process primary gas chromatography mass spectrometric data and store all obtained data in a relational database called MetaSpec which has been developed in-house. Gene knock-outs were observed to strongly influence anabolic and product forming pathways as revealed by different yields of biomass and ethanol as well as by different oxygen consumption profiles. The database information is evaluated using statistical and other bioinformatics tools. The stored information is then linked to existing knowledge, contained in public databases, to predict function of specific genes and their regulatory mechanisms. Furthermore, various heterogenous data sources like protein-protein interaction, common complex formation and co-expression data present in the public domain are being studied to further strengthen the functional links observed among mutant genes based on metabolomics data [2].

Method development

The establishment of a computational framework for the screening experiments of the yeast knock-out strains is the focus of the initial project phase.

Computational Approach

  • Mutant selection. For the initial cultivation and profiling experiments a subset of mutants was selected from the whole set of haploid strains. Each of the selected strains exhibited a deletion of a single gene known or proposed to be involved in the regulation of the central carbon metabolism in S. cerevisiae. The selection considered only genes encoding proteins that have no isoenzymes. The selection procedure involved the exploration of functional information resources (MIPS, KEGG, SGD, STRING, BRENDA) and published research articles.
  • GC-MS Data Processing. In order to accelerate and standardize the procedure for processing of GC-MS spectra, the software tool CalSpec was developed [3]. This tool is now applied for automatic integration of GC-MS spectra involving calculation of mass isotopomer distribution, fractional abundance, total abundance, assessment of peak quality and peak intensity.
  • MetaSpec Database. A MySql database for integration of experimental data was created. The database schema includes GC-MS labeling data, stoichiometric data, oxygen profiles and metabolic flux data structures.
  • Statistical Analysis of Labeling Data. The labeling dataset was studied for recognition of functional signals in the labeling patterns for different mutants. Also intra-fragment and inter-fragment correlations were investigated to recognize relevant features representing the change in the genotype. Mutants with deletions closely linked in the metabolic network were further studied to identify the similarity on the labeling level [4]. The labeling patterns exhibited close correlations between amino acids stemming from the same precursor [5, 6].

Experimental approach

The experimental work is carried out by the group of our collaboration partner Prof. Elmar Heinzle (Biochemical Engineering, Universitaet des Saarlandes).

  • Microtiter Plate Cultivation. In order to obtain quantitative data for phenotypic profiling, reproducible and defined cultivation is essentially required. The suitability of the microtiter plate approach was demonstrated by identical growth behavior of the S. cerevisiae wild-type strain and different mutants to that in conventionally used shake flasks [7].
  • Stoichiometric Profiling. Growth is characterized by the on-line measurement of cell concentration (optical density), by the consumption of the substrate (glucose and other carbohydrates) and by the formation of secreted products (ethanol, acetate). Influences of evaporation of water and dissolved compounds on measured results are corrected for by using dynamic models [8, 9].
  • 13 C-Flux Profiling. The Heinzle group also provides a flux model based on isotopomer model for central carbon metabolism pathways which will be applied for flux estimation of the mutant set. The calculation of metabolic fluxes is based on the 13 C-labeling information stored in the amino acids of the cellular protein which is produced during cultivation on selected 13 C-tracer substrates (e.g., [1-13C] glucose, [1-13C] galactose). The processing of the large amount of data obtained from GC-MS 13C-labeling analysis was accelerated and standardized by a newly developed software tool. [1- 13C] glucose tracer experiments were carried out with all mutants. Clear differences in resulting labeling patterns were observed. Labeling data are used to study interrelationships between different mutants using bioinformatics methods (5). Labeling of amino acids originating from the same precursor metabolite was similar which underlines the consistency of the method [10, 11]. A complete isotopomer model of the central metabolic pathways of yeast was developed based on previous work [11].

Funding source

This project is under going in close collaboration with Biochemical Engineering department of Universitaet des Saarlandes. This project is partly funded by Zentrum für Bioinformatik (ZBI), Saarbrücken


  1. Giaever G, Shoemaker DD, Jones TW, Liang H, Winzeler EA, Astromoff A, Davis RW (1999) Genomic profiling of drug sensitivities via induced haploinsufficiency. Nature Gen. 21:278-283.
  2. Küffner R, Zien, A, Zimmer R, Lengauer T. (2002) Pathway Analysis in Metabolic Databases via Differential Metabolic Display (DMD), Bioinformatics 16, 825-836.
  3. Talwar P, Wittmann C, Lengauer T, Heinzle E. (2003) Software tool for automated processing of 13C labeling data from mass spectrometric spectra. BioTechniques35:1214-5.
  4. Talwar P, Lengauer T, Wittmann C, Mangadu V, Heinzle E. (2003) Towards cellular function through metabolite screening. In: Metabolic Profiling: Pathways in Discovery, Abstract 7, Cambridge Healthtech Institute Conference, 2003.
  5. Talwar P., Lengauer T., Rahnenführer J., Velagapudi V R., Wittmann C., Heinzle E (2004) Computational Methods For Metabolite Screening. In: 5 th International Conference on Systems Biology (ICSB 2004), Heidelberg , Germany .
  6. Talwar P., Lengauer T., Rahnenführer J., Velagapudi V R., Wittmann C., Heinzle E (2004) Computational Methods For Metabolite Screening. In: 12th International Conference on Intelligent Systems for Molecular Biology + 3rd European Conference on Computational Biology (ISMB/ECCB 2004), Glasgow , UK .
  7. John, G.T., Klimant, I., Wittmann, C., Heinzle, E. (2003). Integrated Optical Sensing of Dissolved Oxygen in Microtiter Plates – A Novel Tool for Microbial Cultivation, Biotechnol. Bioeng.81, 829-836.
  8. Kiefer, P., Heinzle E., Zelder, O., Wittmann, C. (2004) Comparative Metabolic Flux Analysis of Lysine-Producing Corynebacterium glutamicum Cultured on Glucose or Fructose. Appl. Env. Microbiol.  70:229-39.
  9. Velagapudi V R, Wittmann C, Talwar P, Lengauer T, Heinzle E (2004) Functional Genomics of Yeast by Metabolic Flux Profiling-Stoichiometry and kinetics of growth and product formation of mutants with deletion of genes in central metabolism. In: 5th International Conference on Systems Biology (ICSB 2004), Heidelberg , Germany .
  10. Wittmann C, Kim HM, Heinzle E. (2004) Metabolic flux analysis of lysine producing Corynebacterium glutamicum at miniaturized scale. Biotechnol. Bioeng., in press.
  11. Wittmann, C., Heinzle, E. (1999) Mass Spectrometry for Metabolic Flux Analysis. Biotechnol. Bioeng. 62, 739-750.