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“Omics” Technologies and Their Input for the Comprehension of Metabolic Systems Particularly Pertaining to Yeast Organisms

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Progress in Botany 72

Part of the book series: Progress in Botany ((BOTANY,volume 72))

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Abstract

“Omics” technologies comprise genomics, transcriptomics, proteomics, metabolomics and phenomics. In this review, these techniques that concentrate on aspects of the “course from genotype to phenotype” are surveyed. With the aid of these global methods, it is possible to combine a collective knowledge of the investigated organism, which is necessary to understand the details of its metabolic system. Hence, the challenge is to introduce the above-mentioned studies for the determination of targets and approaches for the improvement of several organisms. In particular, for yeasts, “omics” technologies can be applied well because research is advanced. For this eukaryotic model organism, an in-depth knowledge is indispensable in order to understand the metabolic fluxes better. Herein, the yeast Saccharomyces cerevisiae as well as brewing yeasts are reviewed with concern to the determination of their “ome” levels.

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References

  • Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207

    Article  CAS  PubMed  Google Scholar 

  • Al-Shahrour F et al (2004) FatiGO: a web tool for finding significant associations of gene ontology terms with groups of genes. Bioinformatics 20:578–580

    Article  CAS  PubMed  Google Scholar 

  • Al-Shahrour F et al (2005a) Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics 21:2988–2993

    Article  CAS  PubMed  Google Scholar 

  • Al-Shahrour F et al (2005b) BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Res 33:W460–W464

    Article  CAS  PubMed  Google Scholar 

  • Al-Shahrour F et al (2006) BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments. Nucleic Acids Res 34:W472–W476

    Article  CAS  PubMed  Google Scholar 

  • Al-Shahrour F et al (2007) FatiGO+: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Res 35:W91–W96

    Article  PubMed  Google Scholar 

  • Ashburner M et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29

    Article  CAS  PubMed  Google Scholar 

  • Bailey JE et al (1990) Strategies and challenges in metabolic engineering a. Ann N Y Acad Sci 589:1–15

    Article  CAS  PubMed  Google Scholar 

  • Bailey JE et al (2002) Inverse metabolic engineering: a strategy for directed genetic engineering of useful phenotypes. Biotechnol Bioeng 79:568–579

    Article  CAS  PubMed  Google Scholar 

  • Benjamini Y, Yekutieli D (2005) Quantitative trait loci analysis using the false discovery rate. Genetics 171:783–790

    Article  CAS  PubMed  Google Scholar 

  • Beranova-Giorgianni S (2003) Proteome analysis by two-dimensional gel electrophoresis and mass spectrometry: strengths and limitations. Trends Anal Chem 22:273–281

    Article  CAS  Google Scholar 

  • Birrell GW et al (2001) A genome-wide screen in S. cerevisiae for genes affecting UV radiation sensitivity. Proc Natl Acad Sci USA 98:12608–12613

    Article  CAS  PubMed  Google Scholar 

  • Blieck L et al (2007) Isolation and characterization of Brewer’s yeast variants with improved fermentation performance under high-gravity conditions. Appl Environ Microbiol 73:815–824

    Article  CAS  PubMed  Google Scholar 

  • Bond U et al (2004) Aneuploidy and copy number breakpoints in the genome of lager yeasts mapped by microarray hybridisation. Curr Genet 45:360–370

    Article  CAS  PubMed  Google Scholar 

  • Boucherie H et al (1995) Two-dimensional protein map of S. cerevisiae: construction of a gene-protein index. Yeast 11:601–613

    Article  CAS  PubMed  Google Scholar 

  • Brejning J et al (2005) Identification of genes and proteins induced during the lag and early exponential phase of lager brewing yeasts. J Appl Microbiol 98:261–271

    Article  CAS  PubMed  Google Scholar 

  • Bro C, Nielsen J (2004) Impact of ‘ome’ analyses on inverse metabolic engineering. Metab Eng 6:204–211

    Article  CAS  PubMed  Google Scholar 

  • Bro C et al (2005) Improvement of galactose uptake in S. cerevisiae through overexpression of phosphoglucomutase: example of transcript analysis as a tool in inverse metabolic engineering. Appl Environ Microbiol 71:6465–6472

    Article  CAS  PubMed  Google Scholar 

  • Cordier H et al (2007) A metabolic and genomic study of engineered S. cerevisiae strains for high glycerol production. Metab Eng 9:364–378

    Article  CAS  PubMed  Google Scholar 

  • Daran-Lapujade P et al (2003) Comparative genotyping of the S. cerevisiae laboratory strains S288C and CEN.PK113-7D using oligonucleotide microarrays. FEMS Yeast Res 4:259–269

    Article  CAS  PubMed  Google Scholar 

  • de Nobel H et al (2001) Parallel and comparative analysis of the proteome and transcriptome of sorbic acid-stressed S. cerevisiae. Yeast 18:1413–1428

    Article  PubMed  Google Scholar 

  • Dequin S (2001) The potential of genetic engineering for improving brewing, wine-making and baking yeasts. Appl Microbiol Biotechnol 56:577–588

    Article  CAS  PubMed  Google Scholar 

  • DeRisi JL et al (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278:680–686

    Article  CAS  PubMed  Google Scholar 

  • Dietvorst J et al (2005) Maltotriose utilization in lager yeast strains: MTT1 encodes a maltotriose transporter. Yeast 22:775–788

    Article  CAS  PubMed  Google Scholar 

  • Donalies U et al (2008) Improvement of Saccharomyces Yeast strains used in brewing, wine making and baking. In Adv Biochem Engin/Food Biotechnology 111:67–98

    Google Scholar 

  • Doniger S et al (2003) MAPPFinder: using gene ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 4:R7

    Article  PubMed  Google Scholar 

  • dos Santos MM et al (2003) Identification of in vivo enzyme activities in the cometabolism of glucose and acetate by S. cerevisiae by using 13C-labeled substrates. Eukaryot Cell 2:599–608

    Article  PubMed  CAS  Google Scholar 

  • Dunn B, Sherlock G (2008) Reconstruction of the genome origins and evolution of the hybrid lager yeast S. pastorianus. Genome Res 18:1610–1623

    Article  CAS  PubMed  Google Scholar 

  • Dziembowski A, Séraphin B (2004) Recent developments in the analysis of protein complexes. FEBS Lett 556:1–6

    Article  CAS  PubMed  Google Scholar 

  • Eisen MB et al (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868

    Article  CAS  PubMed  Google Scholar 

  • Fernandez-Ricaud L et al (2005) PROPHECY-a database for high-resolution phenomics. Nucleic Acids Res 33:D369–D373

    Article  CAS  PubMed  Google Scholar 

  • Gasch AP et al (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11:4241–4257

    CAS  PubMed  Google Scholar 

  • Gavin A-C et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415:141–147

    Article  CAS  PubMed  Google Scholar 

  • Gevaert K, Vandekerckhove J (2009) Reverse-phase diagonal chromatography for phosphoproteome research. In Methods in Molecular Biology/Phospho-Proteomics 527:219–227

    Google Scholar 

  • Giaever G et al (2002) Functional profiling of the S. cerevisiae genome. Nature 418:387–391

    Article  CAS  PubMed  Google Scholar 

  • Gill RT (2003) Enabling inverse metabolic engineering through genomics. Curr Opin Biotechnol 14:484–490

    Article  CAS  PubMed  Google Scholar 

  • Gombert AK et al (2001) Network identification and flux quantification in the central metabolism of S. cerevisiae under different conditions of glucose repression. J Bacteriol 183:1441–1451

    Article  CAS  PubMed  Google Scholar 

  • Gorsich S et al (2006) Tolerance to furfural-induced stress is associated with pentose phosphate pathway genes ZWF1, GND1, RPE1, and TKL1 in S. cerevisiae. Appl Microbiol Biotechnol 71:339–349

    Article  CAS  PubMed  Google Scholar 

  • Graves PR, Haystead TAJ (2002) Molecular biologist’s guide to proteomics. Microbiol Mol Biol Rev 66:39–63

    Article  CAS  PubMed  Google Scholar 

  • Griffin JL (2006) Review. The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? Philos Trans R Soc B: Biol Sci 361:147–161

    Article  CAS  Google Scholar 

  • Griffin TJ et al (2002) Complementary profiling of gene expression at the transcriptome and proteome levels in S. cerevisiae. Mol Cell Proteomics 1:323–333

    Article  CAS  PubMed  Google Scholar 

  • Gygi SP et al (2000) Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci USA 97:9390–9395

    Article  CAS  PubMed  Google Scholar 

  • Haynes PA, Yates JRI (2000) Proteome profiling – pitfalls and progress. Yeast 17:81–87

    Article  CAS  PubMed  Google Scholar 

  • Herrero J et al (2004) New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Res 32:W485–W491

    Article  CAS  PubMed  Google Scholar 

  • Higgins VJ et al (2003a) Yeast genome-wide expression analysis identifies a strong ergosterol and oxidative stress response during the initial stages of an industrial lager fermentation. Appl Environ Microbiol 69:4777–4787

    Article  CAS  PubMed  Google Scholar 

  • Higgins VJ et al (2003b) Application of genome-wide expression analysis to identify molecular markers useful in monitoring industrial fermentations. Appl Environ Microbiol 69:7535–7540

    Article  CAS  PubMed  Google Scholar 

  • Ito T et al (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 98:4569–4574

    Article  CAS  PubMed  Google Scholar 

  • James TC et al (2003) Transcription profile of brewery yeast under fermentation conditions. J Appl Microbiol 94:432–448

    Article  CAS  PubMed  Google Scholar 

  • Jansen R, Gerstein M (2000) Analysis of the yeast transcriptome with structural and functional categories: characterizing highly expressed proteins. Nucleic Acids Res 28:1481–1488

    Article  CAS  PubMed  Google Scholar 

  • Jin Y-S et al (2005) Improvement of xylose uptake and ethanol production in recombinant S. cerevisiae through an inverse metabolic engineering approach. Appl Environ Microbiol 71:8249–8256

    Article  CAS  PubMed  Google Scholar 

  • Joubert R et al (2000) Two-dimensional gel analysis of the proteome of lager brewing yeasts. Yeast 16:511–522

    Article  CAS  PubMed  Google Scholar 

  • Joubert R et al (2001) Identification by mass spectrometry of two-dimensional gel electrophoresis-separated proteins extracted from lager brewing yeast. Electrophoresis 22:2969–2982

    Article  CAS  PubMed  Google Scholar 

  • Kanehisa M et al (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277–D280

    Article  CAS  PubMed  Google Scholar 

  • Kerppola TK (2008) Bimolecular fluorescence complementation (BiFC) analysis as a probe of protein interactions in living cells. Annu Rev Biophys 37:465–487

    Article  CAS  PubMed  Google Scholar 

  • Khatri P, Draghici S (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21:3587–3595

    Article  CAS  PubMed  Google Scholar 

  • Kiemer L, Cesareni G (2007) Comparative interactomics: comparing apples and pears? Trends Biotechnol 25:448–454

    Article  CAS  PubMed  Google Scholar 

  • King OD et al (2003) Predicting gene function from patterns of annotation. Genome Res 13:896–904

    Article  CAS  PubMed  Google Scholar 

  • Kobi D et al (2004) Two-dimensional protein map of an “ale”-brewing yeast strain: proteome dynamics during fermentation. FEMS Yeast Res 5:213–230

    Article  CAS  PubMed  Google Scholar 

  • Kodama Y et al (2006) Lager brewing yeast. In Topics in Current Genetics/Comparative Genomics 15:145–164

    Google Scholar 

  • Kolkman A et al (2005) Development and application of proteomics technologies in S. cerevisiae. Trends Biotechnol 23:598–604

    Article  CAS  PubMed  Google Scholar 

  • Krogan NJ et al (2006) Global landscape of protein complexes in the yeast S. cerevisiae. Nature 440:637–643

    Article  CAS  PubMed  Google Scholar 

  • Lafaye A et al (2005) Combined proteome and metabolite-profiling analyses reveal surprising insights into yeast sulfur metabolism. J Biol Chem 280:24723–24730

    Article  CAS  PubMed  Google Scholar 

  • Lashkari DA et al (1997) Yeast microarrays for genome wide parallel genetic and gene expression analysis. Proc Natl Acad Sci USA 94:13057–13062

    Article  CAS  PubMed  Google Scholar 

  • Liti G et al (2009) Population genomics of domestic and wild yeasts. Nature 458(7236):337–341

    Article  CAS  PubMed  Google Scholar 

  • Lu JP et al (2008). Dual expression recombinase based (DERB) single vector system for high throughput screening and verification of protein interactions in living cells. Available from nature proceedings <http://hdl.handle.net/10101/npre.2008.1550.1>

  • Maaheimo H et al (2001) Central carbon metabolism of S. cerevisiae explored by biosynthetic fractional (13)C labeling of common amino acids. Eur J Biochem 268:2464–2479

    Article  CAS  PubMed  Google Scholar 

  • Maple J, Møller SG (2007) Yeast two-hybrid screening. Methods Mol Biol 362:207–223

    Article  CAS  PubMed  Google Scholar 

  • Marti J et al (2002) Transcriptomes for serial analysis of gene expression. J Soc Biol 196:303–307

    CAS  PubMed  Google Scholar 

  • Meudt HM, Clarke AC (2007) Almost forgotten or latest practice? AFLP applications, analyses and advances. Trends Plant Sci 12:106–117

    Article  CAS  PubMed  Google Scholar 

  • Nakao Y et al (2009) Genome sequence of the lager brewing yeast, an interspecies hybrid. DNA Res 16:115–129

    Article  CAS  PubMed  Google Scholar 

  • Nevoigt E (2008) Progress in metabolic engineering of S. cerevisiae. Microbiol Mol Biol Rev 72:379–412

    Article  CAS  PubMed  Google Scholar 

  • Nie L et al (2008) Statistical application and challenges in global gel-free proteomic analysis by mass spectrometry. Crit Rev Biotechnol 28:297–307

    Article  CAS  PubMed  Google Scholar 

  • Nielsen J (2001) Metabolic engineering. Appl Microbiol Biotechnol 55:263–283

    Article  CAS  PubMed  Google Scholar 

  • Ogata H et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34

    Article  CAS  PubMed  Google Scholar 

  • Olesen K et al (2002) The dynamics of the S. carlsbergensis brewing yeast transcriptome during a production-scale lager beer fermentation. FEMS Yeast Res 2:563–573

    CAS  PubMed  Google Scholar 

  • Oliver SG et al (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16:373–378

    Article  CAS  PubMed  Google Scholar 

  • Oliver SG (2002) Functional genomics: lessons from yeast. Philos Trans R Soc Lond B Biol Sci 357:17–23

    Article  CAS  PubMed  Google Scholar 

  • Olsson L, Nielsen J (2000) The role of metabolic engineering in the improvement of S. cerevisiae: utilization of industrial media. Enzyme Microb Technol 26:785–792

    Article  CAS  PubMed  Google Scholar 

  • Ostergaard S et al (2000) Metabolic engineering of S. cerevisiae. Microbiol Mol Biol Rev 64:34–50

    Article  CAS  PubMed  Google Scholar 

  • Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846

    Article  CAS  PubMed  Google Scholar 

  • Penttilä M (2001) Metabolic engineering approaches-opportunities for brewing Proc Congr Eur Conv Budapest 28:505–513

    Google Scholar 

  • Perrot M et al (1999) Two-dimensional gel protein database of S. cerevisiae (update 1999). Electrophoresis 20:2280–2298

    Article  CAS  PubMed  Google Scholar 

  • Perrot M et al (2007) Yeast proteome map (update 2006). Proteomics 7:1117–1120

    Article  CAS  PubMed  Google Scholar 

  • Petersson A et al (2006) A 5-hydroxymethyl furfural reducing enzyme encoded by the S. cerevisiae ADH6 gene conveys HMF tolerance. Yeast 23:455–464

    Article  CAS  PubMed  Google Scholar 

  • Pfaffl MW (2004) Real-time RT-PCR: Neue Ansätze zur exakten mRNA Quantifizierung. BIOspektrum 1:92–95

    Google Scholar 

  • Pham TK, Wright PC (2007) Proteomic analysis of S. cerevisiae. Expert Rev Proteomics 4:793–813

    Article  PubMed  Google Scholar 

  • Pope GA et al (2007) Metabolic footprinting as a tool for discriminating between brewing yeasts. Yeast 24:667–679

    Article  CAS  PubMed  Google Scholar 

  • Pugh T et al (2002) Global analysis of yeast gene expression during a brewery fermentation. ASBC 2002 Annual Meeting, Savannah, Georgia

    Google Scholar 

  • Puig O et al (2001) The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 24:218–229

    Article  CAS  PubMed  Google Scholar 

  • Rainieri S et al (2006) Pure and mixed genetic lines of S. bayanus and S. pastorianus and their contribution to the lager brewing strain genome. Appl Environ Microbiol 72:3968–3974

    Article  CAS  PubMed  Google Scholar 

  • Ray SS et al (2007) Gene ordering in partitive clustering using microarray expressions. J Biosci 32:1019–1025

    Article  CAS  PubMed  Google Scholar 

  • Reguly T et al (2006) Comprehensive curation and analysis of global interaction networks in S. cerevisiae. J Biol 5:11

    Article  PubMed  Google Scholar 

  • Reiner A et al (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19:368–375

    Article  CAS  PubMed  Google Scholar 

  • Rogowska-Wrzesinska A et al (2001) Comparison of the proteomes of three yeast wild type Strains: CEN.PK2, FY1679 and W303. Comp Funct Genomics 2:207–225

    Article  CAS  PubMed  Google Scholar 

  • Schacherer J et al (2009) Comprehensive polymorphism survey elucidates population structure of S. cerevisiae. Nature 458(7236):342–345

    Article  CAS  PubMed  Google Scholar 

  • Schena M et al (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470

    Article  CAS  PubMed  Google Scholar 

  • Schwikowski B et al (2000) A network of protein–protein interactions in yeast. Nat Biotechnol 18:1257–1261

    Article  CAS  PubMed  Google Scholar 

  • Shakoury-Elizeh M et al (2004) Transcriptional remodeling in response to iron deprivation in S. cerevisiae. Mol Biol Cell 15:1233–1243

    Article  CAS  PubMed  Google Scholar 

  • Shalon D et al (1996) A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res 6:639–645

    Article  CAS  PubMed  Google Scholar 

  • Shoemaker DD et al (1996) Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nat Genet 14:450–456

    Article  CAS  PubMed  Google Scholar 

  • Sickmann A et al (2003) The proteome of S. cerevisiae mitochondria. Proc Natl Acad Sci USA 100:13207–13212

    Article  CAS  PubMed  Google Scholar 

  • Skoneczna A (2006) Decade of genomics-methods for genome investigation in yeast S. cerevisiae. Postepy Biochem 52:435–447

    CAS  PubMed  Google Scholar 

  • Smedsgaard J, Nielsen J (2005) Metabolite profiling of fungi and yeast: from phenotype to metabolome by MS and informatics. J Exp Bot 56:273–286

    Article  CAS  PubMed  Google Scholar 

  • Sung M-K, Huh W-K (2007) Bimolecular fluorescence complementation analysis system for in vivo detection of protein–protein interaction in S. cerevisiae. Yeast 24:767–775

    Article  CAS  PubMed  Google Scholar 

  • Tang T et al (2007) Expression ratio evaluation in two-colour microarray experiments is significantly improved by correcting image misalignment. Bioinformatics 23:2686–2691

    Article  CAS  PubMed  Google Scholar 

  • ter Linde JJM et al (1999) Genome-wide transcriptional analysis of aerobic and anaerobic chemostat cultures of S. cerevisiae. J Bacteriol 181:7409–7413

    PubMed  Google Scholar 

  • Uetz P et al (2000) A comprehensive analysis of protein–protein interactions in S. cerevisiae. Nature 403:623–627

    Article  CAS  PubMed  Google Scholar 

  • van Damme P et al (2008) Protein processing characterized by a gel-free proteomics approach. In Methods in Molecular Biology/ Functional Proteomics 484:245–262

    Google Scholar 

  • Vaquerizas JM et al (2005) GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res 33:W616–W620

    Article  CAS  PubMed  Google Scholar 

  • Velculescu VE et al (1995) Serial analysis of gene expression. Science 270:484–487

    Article  CAS  PubMed  Google Scholar 

  • Velculescu VE et al (1997) Characterization of the yeast transcriptome. Cell 88:243–251

    Article  CAS  PubMed  Google Scholar 

  • Vos P et al (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23:4407–4414

    Article  CAS  PubMed  Google Scholar 

  • Warringer J, Blomberg A (2003) Automated screening in environmental arrays allows analysis of quantitative phenotypic profiles in S. cerevisiae. Yeast 20:53–67

    Article  CAS  PubMed  Google Scholar 

  • Warringer J et al (2003) High-resolution yeast phenomics resolves different physiological features in the saline response. Proc Natl Acad Sci USA 100:15724–15729

    Article  CAS  PubMed  Google Scholar 

  • Watanabe T et al (2004) A new approach to species determination for yeast strains: DNA microarray-based comparative genomic hybridization using a yeast DNA microarray with 6000 genes. Yeast 21:351–365

    Article  CAS  PubMed  Google Scholar 

  • Wilkins MR et al (1996) From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnol Bioeng 14:61–65

    CAS  Google Scholar 

  • Winzeler EA et al (1999a) Whole genome genetic-typing in yeast using high-density oligonucleotide arrays. Parasitology 118:73–80

    Article  Google Scholar 

  • Winzeler EA et al (1999b) Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285:901–906

    Article  CAS  PubMed  Google Scholar 

  • Wu J et al (2004) Global analysis of nutrient control of gene expression in S. cerevisiae during growth and starvation. Proc Natl Acad Sci USA 101:3148–3153

    Article  CAS  PubMed  Google Scholar 

  • Yale J, Bohnert HJ (2001) Transcript expression in S. cerevisiae at high salinity. J Biol Chem 276:15996–16007

    Article  CAS  PubMed  Google Scholar 

  • Yoshimoto H et al (2002) Genome-wide analysis of gene expression regulated by the calcineurin/Crz1p signaling pathway in S. cerevisiae. J Biol Chem 277:31079–31088

    Article  CAS  PubMed  Google Scholar 

  • Young KH (1998) Yeast two-hybrid: so many interactions, (in) so little time. Biol Reprod 58:302–311

    Article  CAS  PubMed  Google Scholar 

  • Yu H et al (2008) High-quality binary protein interaction map of the yeast interactome network. Science 322:104–110

    Article  CAS  PubMed  Google Scholar 

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  1. Institute for Biotechnology, Berlin University of Technology, 13353, Berlin, Germany

    L. Strack & U. Stahl

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  1. L. Strack
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  2. U. Stahl
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Editors and Affiliations

  1. Inst. Botanik, TU Darmstadt, Schnittspahnstr. 3-5, Darmstadt, 64287, Germany

    Ulrich E. Lüttge

  2. Fakultät für Biologie, LS für Experimentelle Ökologie, Universität Bielefeld, Bielefeld, Germany

    Wolfram Beyschlag

  3. FB Biologie, Abt. Allgemeine Botanik, TU Kaiserslautern, Erwin-Schrödinger-Str., Kaiserslautern, 67663, Germany

    Burkhard Büdel

  4. Cardiff School of Biosciences, University of Cardiff, Cardiff, United Kingdom

    Dennis Francis

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Strack, L., Stahl, U. (2010). “Omics” Technologies and Their Input for the Comprehension of Metabolic Systems Particularly Pertaining to Yeast Organisms. In: Lüttge, U., Beyschlag, W., Büdel, B., Francis, D. (eds) Progress in Botany 72. Progress in Botany, vol 72. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13145-5_4

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