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Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data

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Yeast Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 759))

Abstract

One of the major objectives of systems biology is the development of mathematical models for the quantitative description of complex biological systems, such as living cells. Biological data and software tools for the design, analysis, and simulation of models are two basic ingredients for the new field of systems biology. In this chapter we give an overview of databases and repositories that provide valuable information for the integrative analysis and modeling of data generated by the different omics techniques. We also provide a review of the most popular software tools currently used in computational systems biology studies. Standards for the annotation of biological data and for the analysis and exchange of models are fundamental for the success of systems biology and provide the glue that connects experimental data with mathematical models. We also discuss some broad trends regarding where systems biology is heading to.

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References

  1. Hubbard, T., Barker, D., Birney, E., et al. (2002) The Ensembl genome database project. Nucleic Acids Res. 30, 38–41.

    Article  PubMed  CAS  Google Scholar 

  2. Boeckmann, B., Bairoch, A., Apweiler, R., et al. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370.

    Article  PubMed  CAS  Google Scholar 

  3. Apweiler, R., Bairoch, A., Wu, C. H., et al. (2004) UniProt: the universal protein knowledgebase. Nucleic Acids Res. 32, D115–119.

    Article  PubMed  CAS  Google Scholar 

  4. Biswas, M., O’Rourke, J. F., Camon, E., et al. (2002) Applications of InterPro in protein annotation and genome analysis. Brief Bioinform. 3, 285–295.

    Article  PubMed  CAS  Google Scholar 

  5. Mulder, N. J., Apweiler, R., Attwood, T. K., et al. (2003) The InterPro database 2003 brings increased coverage and new features. Nucleic Acids Res. 31, 315–318.

    Article  PubMed  CAS  Google Scholar 

  6. Berman, H. M., Westbrook, J., Feng, Z., et al. (2003) The protein data bank. Nucleic Acids Res. 28, 235–242.

    Article  Google Scholar 

  7. Cherry, J. M., Ball, C., Weng, S., et al. (1997) Genetic and physical maps of Saccharomyces cerevisiae. Nature 387(6632 Suppl), 67–73.

    PubMed  CAS  Google Scholar 

  8. Galperin, M. Y., and Cochrane, G. R. (2009) Nucleic acids research annual database issue and the NAR online molecular biology database collection in 2009. Nucleic Acids Res. 37, D1–4.

    Article  PubMed  CAS  Google Scholar 

  9. Barrett, T., Troup, D. B., Wilhite, S. E., et al. (2007) NCBI GEO: mining tens of millions of expression profiles – database and tools update. Nucleic Acids Res. 35, D760–765.

    Article  PubMed  CAS  Google Scholar 

  10. Brazma, A., Parkinson, H., Sarkans, U., et al. (2003) ArrayExpress – a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71.

    Article  PubMed  CAS  Google Scholar 

  11. Karp, P. D., Ouzounis, C. A., Moore-Kochlacs, C., et al. (2005) Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 19, 6083–6089.

    Article  Google Scholar 

  12. Kanehisa, M., Araki, M., Goto, S., et al. (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–484.

    Article  PubMed  CAS  Google Scholar 

  13. Matthews, L., Gopinath, G., Gillespie, M., et al. (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37, D619–622.

    Article  PubMed  CAS  Google Scholar 

  14. Güldener, U., Münsterkötter, M., Oesterheld, M., et al. (2006) MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Res. 34, D436–441.

    Article  PubMed  Google Scholar 

  15. Kerrien, S., Alam-Faruque, Y., Aranda, B., et al. (2007) IntAct – open source resource for molecular interaction data. Nucleic Acids Res. 35, D561–565.

    Article  PubMed  CAS  Google Scholar 

  16. Salwinski, L., Miller, C. S., Smith, A. J. et al. (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res. 32, D449–451.

    Article  PubMed  CAS  Google Scholar 

  17. Chatr-aryamontri, A., Ceol, A., Palazzi, L. M., et al. (2007) MINT: the Molecular INTeraction database. Nucleic Acids Res. 35, D572–574.

    Article  PubMed  CAS  Google Scholar 

  18. Kamburov, A., Wierling, C., Lehrach, H., and Herwig, R. (2009) ConsensusPathDB – a database for integrating human functional interaction networks. Nucleic Acids Res. 37, D623–628.

    Article  PubMed  CAS  Google Scholar 

  19. Chang, A., Scheer, M., Grote, A., Schomburg, I., and Schomburg, D. (2009) BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res. 37, D588–592.

    Article  PubMed  CAS  Google Scholar 

  20. Wittig, U., Golebiewski, M., Kania, R., et al. (2006) SABIO-RK: integration and curation of reaction kinetics data. In: Leser, U., Naumann, F., and Eckman, B. (eds.), Proceedings of the 3rd International workshop on Data Integration in the Life Sciences 2006 (DILS’06). Hinxton, UK. Lecture Notes in Bioinformatics Vol. 4075, pp. 94–103. Berlin, Heidelberg: Springer.

    Google Scholar 

  21. Rojas, I., Golebiewski, M., Kania, R., et al. (2007) SABIO-RK: a database for biochemical reactions and their kinetics. BMC Syst. Biol. 1(Suppl 1), S6.

    Article  Google Scholar 

  22. Oliver, B. G., and Snoep, J. L. (2004) Web-based kinetic modelling using JWS Online. Bioinformatics 20, 2143–2144.

    Article  Google Scholar 

  23. Le Novere, N., Bornstein, B., Broicher, A., et al. (2006) BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res. 34, D689–691.

    Article  PubMed  CAS  Google Scholar 

  24. Brazma, A., Krestyaninova, M., and Sarkans, U. (2006) Standards for systems biology. Nat. Rev. Genet. 7, 593–605.

    Article  PubMed  CAS  Google Scholar 

  25. Brazma, A., Hingamp, P., Quarckenbush, J., et al. (2001) Minimum information about a microarray experiment (MIAME) – towards standards for microarray data. Nat. Genet. 29, 365–371.

    Article  PubMed  CAS  Google Scholar 

  26. Taylor, C. F., Paton, N. W., Lilley, K. S., et al. (2007) The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 25, 887–893.

    Article  PubMed  CAS  Google Scholar 

  27. Le Novere, N., Finney, A., Hucka, M., et al. (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat. Biotechnol. 23, 1509–1515.

    Article  PubMed  CAS  Google Scholar 

  28. Hucka, M., Finney, A., Sauro, H. M., et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531.

    Article  PubMed  CAS  Google Scholar 

  29. Hucka, M., Finney, A., Bornstein, B. J., et al. (2004) Evolving a lingua franca and associated software infrastructure for computational systems biology: the systems biology markup language (SBML) project. Syst Biol. 1, 41–53.

    Article  CAS  Google Scholar 

  30. Lloyd, C. M., Halstead, M. D. B., and Nielsen, P. F. (2004) CellML: its future, present and past. Prog. Biophys. Mol. Biol. 85, 433–450.

    Article  PubMed  CAS  Google Scholar 

  31. Luciano, J. S. (2005) PAX of mind for pathway researchers. Drug Discov. Today 10, 937–942.

    Article  PubMed  CAS  Google Scholar 

  32. Hermjakob, H., Montecchi-Palazzi, L., Bader, G., et al. (2004) The HUPO PSI’s molecular interaction format – a community standard for the representation of protein interaction data. Nat. Biotechnol. 22, 177–183.

    Article  PubMed  CAS  Google Scholar 

  33. Hull, D., Wolstencroft, K., Stevens, R., et al. (2006) Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 34 (Web Server issue), 729–732.

    Article  Google Scholar 

  34. Klipp, E., Liebermeister, W., Helbig, A., Kowald, A., and Schaber, J. (2007) Systems biology standards – the community speaks. Nat. Biotechnol. 25, 390–391.

    Article  PubMed  CAS  Google Scholar 

  35. Klipp, E., Herwig, R., Kowald, A., Wierling, C., and Lehrach, H. (2005) Systems Biology in Practice. Weinheim: Wiley-VCH.

    Book  Google Scholar 

  36. Funahashi, A., Matsuoka, Y., Jouraku, A., Morohashi, M., Kikuchi, N., and Kitano, H. (2008) CellDesigner 3.5: a versatile modeling tool for biochemical networks. ProcIEEE 96, 1254–1265.

    Google Scholar 

  37. Hoops, S., Sahle, S., Gauges, R., et al. (2006) COPASI – a COmplex PAthway SImulator. Bioinformatics 22, 3067–3074.

    Article  PubMed  CAS  Google Scholar 

  38. Lee, D., Saha, R., Yusufi, F. N. K., Park, W., and Karimi, I. A. (2008) Web-based applications for building, managing and analysing kinetic models of biological systems. Brief Bioinform. 10, 65–74.

    Article  PubMed  Google Scholar 

  39. Wierling, C., Herwig, R., and Lehrach, H. (2007) Resources, standards and tools for systems biology. Brief Funct. Genomic Proteomic 6, 240–251.

    Article  PubMed  CAS  Google Scholar 

  40. Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., and Herwig, R. (2009) Systems Biology. A Textbook. Weinheim: Wiley-VCH.

    Google Scholar 

  41. Levin, B. (1999) Genes VII. Oxford: Oxford University Press.

    Google Scholar 

  42. Gillespie, D. T. (2001) Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115, 1716–1733.

    Article  CAS  Google Scholar 

  43. Gillespie, D. T. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361.

    Article  CAS  Google Scholar 

  44. Gibson, M. A., and Bruck, J. (2000) Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. 104, 1876–1889.

    Article  CAS  Google Scholar 

  45. Ramsey, S., and Orrell, D. (2005) Dizzy: stochastic simulations of large-scale genetic regulatory networks. J. Bioinform. Comput. Biol. 3, 1–21.

    Article  Google Scholar 

  46. Kowald, A., Jendrach, M., Pohl, S., Bereiter-Hahn, J., and Hammerstein, P. (2005) On the relevance of mitochondrial fusions for the accumulation of mitochondrial deletion mutants: a modelling study. Aging Cell 4, 273–283.

    Article  PubMed  CAS  Google Scholar 

  47. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J., and Kitano, H. (2002) The ERATO systems biology workbench: enabling interaction and exchange between software tools for computational biology. Pac. Symp. Biocomput. 450–461.

    Google Scholar 

  48. Hattne, J., Fange, D., and Elf, J. (2005) Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics 21, 2923–2924.

    Article  PubMed  CAS  Google Scholar 

  49. Sauro, H. M., Hucka, M., Finney, A., et al. (2003) Next generation simulation tools: the systems biology workbench and BioSPICE integration. OMICS 7, 355–372.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

This work was supported by the German Federal Ministry of Education and Research (BMBF) with the project Mutanom (01GS08105).

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Authors and Affiliations

  1. Humboldt University Berlin, Institute for Biology, Theoretical Biophysics, Invalidenstr. 42, 10115, Berlin, Germany

    Axel Kowald

  2. Max Planck Institute for Molecular Genetics, Berlin, Germany

    Christoph Wierling

Authors
  1. Axel Kowald
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  2. Christoph Wierling
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Corresponding author

Correspondence to Christoph Wierling .

Editor information

Editors and Affiliations

  1. , Department of Biochemistry, University of Cambridge, Tennis Court Road 80, Cambridge, CB2 1GA, United Kingdom

    Juan I. Castrillo

  2. , Department of Biochemistry, University of Cambridge, Tennis Court Road 80, Cambridge, CB2 1GA, United Kingdom

    Stephen G. Oliver

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© 2011 Humana Press

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Kowald, A., Wierling, C. (2011). Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data. In: Castrillo, J., Oliver, S. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 759. Humana Press. https://doi.org/10.1007/978-1-61779-173-4_20

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  • DOI: https://doi.org/10.1007/978-1-61779-173-4_20

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-172-7

  • Online ISBN: 978-1-61779-173-4

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