Skip to main content
SpringerLink
Log in

Standards, Databases, and Modeling Tools in Systems Biology

  • Protocol
  • First Online:
Data Mining in Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 696))

Abstract

Modeling is a means for integrating the results from Genomics, Transcriptomics, Proteomics, and Metabolomics experiments and for gaining insights into the interaction of the constituents of biological systems. However, sharing such large amounts of frequently heterogeneous and distributed experimental data needs both standard data formats and public repositories. Standardization and a public storage system are also important for modeling due to the possibility of sharing models irrespective of the used software tools. Furthermore, rapid model development strongly benefits from available software packages that relieve the modeler of recurring tasks like numerical integration of rate equations or parameter estimation.

In this chapter, the most common standard formats used for model encoding and some of the major public databases in this scientific field are presented. The main features of currently available modeling software are discussed and proposals for the application of such tools are given.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nat Genet 29:365–371

    Article  CAS  PubMed  Google Scholar 

  2. Spellman P, Miller M, Stewart J, Troup C, Sarkans U, Chervitz S, Bernhart D, Sherlock G, Ball C, Lepage M, Swiatek M, Marks WL, Goncalves J, Markel S, Iordan D, Shojatalab M, Pizarro A, White J, Hubley R, Deutsch E, Senger M, Aronow B, Robinson A, Bassett D, Stoeckert C, Brazma A (2002) Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol 3:research0046.1–0046.9

    Article  Google Scholar 

  3. Deutsch E (2008) mzML: a single, unifying data format for mass spectrometer output. Proteomics 8:2776–2777

    Article  CAS  PubMed  Google Scholar 

  4. Martens L, Hermjakob H, Jones P, Adamski M, Taylor C, States D, Gevaert K, Vandekerckhove J, Apweiler R (2005) PRIDE: the proteomics identifications database. Proteomics 5:3537–3545

    Article  CAS  PubMed  Google Scholar 

  5. Eisenacher M, Martens L, Hardt T, Kohl M, Barsnes H, Helsens K, Hakkinen J, Levander F, Aebersold R, Vandekerckhove J, Dunn MJ, Lisacek F, Siepen JA, Hubbard SJ, Binz PA, Bluggel M, Thiele H, Cottrell J, Meyer HE, Apweiler R, Stephan C (2009) Getting a grip on proteomics data - proteomics data collection (ProDaC). Proteomics 9:3928–3933

    Article  CAS  PubMed  Google Scholar 

  6. Taylor CF, Paton NW, Lilley KS, Binz PA, Julian RK, Jones AR, Zhu WM, Apweiler R, Aebersold R, Deutsch EW, Dunn MJ, Heck AJR, Leitner A, Macht M, Mann M, Martens L, Neubert TA, Patterson SD, Ping PP, Seymour SL, Souda P, Tsugita A, Vandekerckhove J, Vondriska TM, Whitelegge JP, Wilkins MR, Xenarios I, Yates JR, Hermjakob H (2007) The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 25:887–893

    Article  CAS  PubMed  Google Scholar 

  7. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novere N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531

    Article  CAS  PubMed  Google Scholar 

  8. Hucka M, Finney A, Bornstein BJ, Keating SM, Shapiro BE, Matthews J, Kovitz BL, Schilstra MJ, Funahashi A, Doyle JC, Kitano H (2004) Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. Syst Biol (Stevenage) 1:41–53

    Article  CAS  Google Scholar 

  9. Finney A, Hucka M (2003) Systems biology markup language: Level 2 and beyond. Biochem Soc T 31:1472–1473

    Article  CAS  Google Scholar 

  10. Garny A, Nickerson DP, Cooper J, dos Santos RW, Miller AK, McKeever S, Nielsen PMF, Hunter PJ (2008) CellML and associated tools and techniques. Philos T R Soc A 366:3017–3043

    Article  Google Scholar 

  11. Lloyd CM, Halstead MD, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85:433–450

    Article  CAS  PubMed  Google Scholar 

  12. Cuellar AA, Lloyd CM, Nielsen PF, Bullivant DP, Nickerson DP, Hunter PJ (2003) An overview of CellML 1.1, a biological model description language. Simul-T Soc Mod Sim 79:740–747

    Google Scholar 

  13. Luciano JS (2005) PAX of mind for pathway researchers. Drug Discov Today 10:937–942

    Article  CAS  PubMed  Google Scholar 

  14. Goddard NH, Hucka M, Howell F, Cornelis H, Shankar K, Beeman D (2001) Towards NeuroML: model description methods for collaborative modelling in neuroscience. Philos T Roy Soc B 356:1209–1228

    Article  CAS  Google Scholar 

  15. Le Novere N, Finney A, Hucka M, Bhalla US, Campagne F, Collado-Vides J, Crampin EJ, Halstead M, Klipp E, Mendes P, Nielsen P, Sauro H, Shapiro B, Snoep JL, Spence HD, Wanner BL (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515

    Article  PubMed  Google Scholar 

  16. Serrano L (2007) Synthetic biology: promises and challenges. Mol Syst Biol 3:158

    Article  PubMed  Google Scholar 

  17. Le Novere N, Courtot M, Laibe C (2006) Adding semantics in kinetics models of biochemical pathways. Ruedesheim, Germany, pp 137–153

    Google Scholar 

  18. Le Novere N (2006) Model storage, exchange and integration. BMC Neurosci 7:S1

    Article  Google Scholar 

  19. Le Novere N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, Li L, Sauro H, Schilstra M, Shapiro B, Snoep JL, Hucka M (2006) BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 34:D689–D691

    Article  PubMed  Google Scholar 

  20. Olivier BG, Snoep JL (2004) Web-based kinetic modelling using JWS online. Bioinformatics 20:2143–2144

    Article  CAS  PubMed  Google Scholar 

  21. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354–D357

    Article  CAS  PubMed  Google Scholar 

  22. Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:27–30

    Article  CAS  PubMed  Google Scholar 

  23. Lloyd CM, Lawson JR, Hunter PJ, Nielsen PF (2008) The CellML model repository. Bioinformatics 24:2122–2123

    Article  CAS  PubMed  Google Scholar 

  24. Bhalla US (2002) Use of Kinetikit and GENESIS for modeling signaling pathways. Method Enzymol 345:3–23

    Article  Google Scholar 

  25. Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM (2004) ModelDB: a database to support computational neuroscience. J Comput Neurosci 17:7–11

    Article  PubMed  Google Scholar 

  26. Campagne F, Neves S, Chang CW, Skrabanek L, Ram PT, Iyengar R, Weinstein H (2004) Quantitative information management for the biochemical computation of cellular networks. Sci STKE 248:pl11

    Article  Google Scholar 

  27. Alves R, Antunes F, Salvador A (2006) Tools for kinetic modeling of biochemical networks. Nat Biotechnol 24:667–672

    Article  CAS  PubMed  Google Scholar 

  28. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U (2006) COPASI- A COmplex PAthway SImulator. Bioinformatics 22:3067–3074

    Article  CAS  PubMed  Google Scholar 

  29. Ramsey S, Orrell D, Bolouri H (2005) Dizzy: stochastic simulation of large-scale genetic regulatory networks. J Bioinform Comput Biol 3:415–436

    Article  CAS  PubMed  Google Scholar 

  30. Hucka M, Finney A, Sauro H, Kovitz B, Keating S, Matthews J, Bolouri H (2003) Introduction to the Systems Biology Workbench. Available via the World Wide Web at http://sbw.kgi.edu/caltechSBW/sbwDocs/docs/intro/intro.pdf.

  31. Sauro HM, Hucka M, Finney A, Wellock C, Bolouri H, Doyle J, Kitano H (2003) Next generation simulation tools: the systems biology workbench and BioSPICE integration. OMICS 7:355–372

    Article  CAS  PubMed  Google Scholar 

  32. Takahashi K, Ishikawa N, Sadamoto Y, Sasamoto H, Ohta S, Shiozawa A, Miyoshi F, Naito Y, Nakayama Y, Tomita M (2003) E-cell 2: Multi-platform E-Cell simulation system. Bioinformatics 19:1727–1729

    Article  CAS  PubMed  Google Scholar 

  33. Tomita M, Hashimoto K, Takahashi K, Matsuzaki Y, Matsushima R, Saito K, Yugi K, Miyoshi F, Nakano H, Tanida S, Saito Y, Kawase A, Watanabe N, Shimizu TS, Nakayama Y (2000) The E-CELL project: towards integrative simulation of cellular processes. New Generat Comput 18:1–12

    Article  Google Scholar 

  34. Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter JC, Hutchison CA (1999) E-CELL: software environment for whole-cell simulation. Bioinformatics 15:72–84

    Article  CAS  PubMed  Google Scholar 

  35. You LC, Hoonlor A, Yin J (2003) Modeling biological systems using Dynetica - a simulator of dynamic networks. Bioinformatics 19:435–436

    Article  CAS  PubMed  Google Scholar 

  36. Dhar P, Meng TC, Somani S, Ye L, Sairam A, Chitre M, Hao Z, Sakharkar K (2004) Cellware - a multi-algorithmic software for computational systems biology. Bioinformatics 20:1319–1321

    Article  CAS  PubMed  Google Scholar 

  37. Dhar PK, Meng TC, Somani S, Ye L, Sakharkar K, Krishnan A, Ridwan ABM, Wah SHK, Chitre M, Hao Z (2005) Grid Cellware: the first grid-enabled tool for modelling and simulating cellular processes. Bioinformatics 21:1284–1287

    Article  CAS  PubMed  Google Scholar 

  38. Slepchenko BM, Schaff JC, Macara I, Loew LM (2003) Quantitative cell biology with the virtual cell. Trends Cell Biol 13:570–576

    Article  CAS  PubMed  Google Scholar 

  39. Moraru II, Schaff JC, Slepchenko BM, Blinov ML, Morgan F, Lakshminarayana A, Gao F, Li Y, Loew LM (2008) Virtual Cell modelling and simulation software environment. IET Syst Biol 2:352–362

    Article  CAS  PubMed  Google Scholar 

  40. Schilstra MJ, Li L, Matthews J, Finney A, Hucka M, Le Novere N (2006) CellML2SBML: conversion of CellML into SBML. Bioinformatics 22:1018–1020

    Article  CAS  PubMed  Google Scholar 

  41. Bornstein BJ, Keating SM, Jouraku A, Hucka M (2008) LibSBML: an API library for SBML. Bioinformatics 24:880–881

    Article  CAS  PubMed  Google Scholar 

  42. Nicolas R, Donizelli M, Le Novere N (2007) SBMLeditor: effective creation of models in the Systems Biology Markup Language (SBML). BMC Bioinform 8:79

    Article  Google Scholar 

  43. Christie GR, Nielsen PMF, Blackett SA, Bradley CP, Hunter PJ (2009) FieldML: concepts and implementation. Philos T R Soc A 367:1869–1884

    Article  Google Scholar 

  44. Chickarmane V, Paladugu SR, Bergmann F, Sauro HM (2005) Bifurcation discovery tool. Bioinformatics 21:3688–3690

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Michael Kohl is funded by the Bundesministerium für Bildung und Forschung (BMBF), grant 01 GS 08143.

Author information

Authors and Affiliations

  1. Medizinisches Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany

    Michael Kohl

Authors
  1. Michael Kohl
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Lead Discovery Center GmbH, Emil-Figge-Straße 76a76a, Dortmund, 44227, Germany

    Michael Hamacher

  2. Medizinisches Proteom-Center, Ruhr-Universität Bochum, Universitätsstraße 150, Bochum, 44801, Germany

    Martin Eisenacher

  3. Medizinisches Proteom-Center, Ruhr-Universität Bochum, Universitätsstraße 150, Bochum, 44801, Germany

    Christian Stephan

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Kohl, M. (2011). Standards, Databases, and Modeling Tools in Systems Biology. In: Hamacher, M., Eisenacher, M., Stephan, C. (eds) Data Mining in Proteomics. Methods in Molecular Biology, vol 696. Humana Press. https://doi.org/10.1007/978-1-60761-987-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-1-60761-987-1_26

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-60761-986-4

  • Online ISBN: 978-1-60761-987-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation