Skip to main content
SpringerLink
Log in

Modeling Languages for Biochemical Network Simulation: Reaction vs Equation Based Approaches

  • Chapter
  • First Online:
Biosystems Engineering II

Part of the book series: Advances in Biochemical Engineering / Biotechnology ((ABE,volume 121))

Abstract

Biochemical network modeling and simulation is an essential task in any systems biology project. The systems biology markup language (SBML) was established as a standardized model exchange language for mechanistic models. A specific strength of SBML is that numerous tools for formulating, processing, simulation and analysis of models are freely available. Interestingly, in the field of multidisciplinary simulation, the problem of model exchange between different simulation tools occurred much earlier. Several general modeling languages like Modelica have been developed in the 1990s. Modelica enables an equation based modular specification of arbitrary hierarchical differential algebraic equation models. Moreover, libraries for special application domains can be rapidly developed. This contribution compares the reaction based approach of SBML with the equation based approach of Modelica and explains the specific strengths of both tools. Several biological examples illustrating essential SBML and Modelica concepts are given. The chosen criteria for tool comparison are flexibility for constraint specification, different modeling flavors, hierarchical, modular and multidisciplinary modeling. Additionally, support for spatially distributed systems, event handling and network analysis features is discussed. As a major result it is shown that the choice of the modeling tool has a strong impact on the expressivity of the specified models but also strongly depends on the requirements of the application context.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Abbreviations

DAE:

differential algebraic equation

PDAE:

partial differential algebraic equation

PDE:

partial differential equation

SBML:

systems biology markup language

XML:

extended markup language

References

  1. von Bertalanffy L (1932) Theoretische biologie. Bd. 1: allgemeine theorie, physikochemie, aufbau und entwicklung des organismus. Gebrüder Borntraeger, Berlin

    Google Scholar 

  2. Kitano H (2001) Foundations of systems biology. MIT Press, Cambridge

    Google Scholar 

  3. Klipp E, Herwig R, Kowald A et al (2005) Systems biology in practice. Wiley-VCH, Weinheim

    Book  Google Scholar 

  4. Heinrich R, Schuster S (1996) The regulation of cellular systems. Kluwer Academic, Dordrecht

    Book  Google Scholar 

  5. Snoep JL (2005) The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level. Curr Opin Biotechnol 16:336–343

    Article  CAS  Google Scholar 

  6. Hucka M, Finney A, Sauro H et al. (2001) Systems biology markup language (SBML) level 1: structures and facilities for basic model definitions. www.sbml.org

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

    Article  CAS  Google Scholar 

  8. Vladimirescu A (1994) The SPICE book. Wiley, NY

    Google Scholar 

  9. Cellier FE (1991) Continuous system modeling. Springer, NY

    Book  Google Scholar 

  10. Booch G (1990) Object oriented design with applications. Benjamin-Cummings, CA

    Google Scholar 

  11. Ashenden PJ, Peterson GD, Teegarden DA (2002) The systems designers guide to VHDL-AMS. Morgan Kaufmann, CA

    Google Scholar 

  12. Fritzson P (2003) Principles of object-oriented modeling and simulation with Modelica 2.1. Wiley-IEEE Computer Society, New York

    Google Scholar 

  13. Ginkel M, Kremling A, Nutsch T et al (2003) Modular modeling of cellular systems with ProMoT/Diva. Bioinformatics 19:1169–1176

    Article  CAS  Google Scholar 

  14. Mann H (1994) Equation formulation and solution methods behind DYNAST – a multipurpose simulation tool. In: IMACS symp. on mathematical modelling MATHMOD, Vienna, pp 938–939

    Google Scholar 

  15. Bill E, Kent S, Thiru T et al (2007) Professional XML. Wiley-VCH, Heidelberg

    Google Scholar 

  16. Campbell KA (2002) Xerlin 1.1 user guide. www.xerlin.org

  17. Funahashi A, Tanimura N, Morohashi M et al (2003) CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. BIOSILICO 1:159–162

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Systems biology markup language. www.sbml.org

  20. Hucka M, Hoops S, Keating SM et al (2008) Systems biology markup language (SBML) level 2: structures and facilities for model definitions. www.sbml.org

  21. EUROSYSLIB Modelica libraries for embedded systems modelling and simulation. http://www.itea2.org/public/project_leaflets/EUROSYSLIB_profile_oct-07.pdf

  22. Elmqvist H (1993) Object-oriented modeling and automatic formula manipulation in Dymola. In: SIMS’93, Scandinavian Simulation Society, Kongsberg, Norway

    Google Scholar 

  23. Maffezzoni C, Girelli R, Lluka P (1996) Generating efficient computational procedures from declarative models. Simul Pract Theory 4:303–317

    Article  Google Scholar 

  24. Murota K (1987) Systems analysis by graphs and matroids. Springer, Berlin

    Book  Google Scholar 

  25. Lieres E, Petersen S, Wiechert W (2004) A multi-scale modeling concept and computational tools for the integrative analysis of stationary metabolic data, vol 1. J Integr Bioinform

    Google Scholar 

  26. Cellier FE, Ernesto K (2006) Continuous system simulation. Springer, New York

    Google Scholar 

  27. Ascher UM, Petzold LR (1998) Computer methods for ordinary differential equations and differential algebraic equations. SIAM, Philadelphia

    Book  Google Scholar 

  28. Leitold A, Hangos KM (2001) Structural solvability analysis of dynamic process models. Comput Chem Eng 25:1633–1646

    Article  CAS  Google Scholar 

  29. Mattsson SE, Söderlind G (1993) Index reduction in differential-algebraic equations using dummy derivatives. SIAM J Sci Comput 14:677–692

    Article  Google Scholar 

  30. Pantelides CC (1988) The consistent initialization of differential-algebraic systems. SIAM J Sci Stat Comput 9:213–231

    Article  Google Scholar 

  31. Kremling A, Jahreis K, Lengeler JW et al (2000) The organization of metabolic reaction networks: a signal-oriented approach to cellular models. Metab Eng 2:190–200

    Article  CAS  Google Scholar 

  32. Kremling A, Gilles ED (2001) The organization of metabolic reaction networks. II. Signal processing in hierarchical structured functional units. Metab Eng 3:138–150

    Article  CAS  Google Scholar 

  33. Olsson H, Tummescheit H, Elmqvist H (2005) Using automatic differentiation for partial derivatives of functions in Modelica. In: Modelica 2005, Hamburg, Germany, pp 105–112

    Google Scholar 

  34. Saldamli L, Bachmann B, Fritzson P (2005) A framework for describing and solving PDE models in Modelica. In: Modelica 2005, Hamburg, Germany, pp 113–122

    Google Scholar 

  35. Tiller M (2001) Introduction to physical modeling with Modelica. Kluwer Academic, Norwell

    Google Scholar 

  36. Wiechert W, Treude M (2007) Teaching finite elements: an alternative approach using Modelica. In: EUROSIM, Lubljana

    Google Scholar 

  37. Plank J (1997) State events in continuous modelling and simulation – concepts, implementation and new methodology. In: ASIM/ARGESIM/SCS (ed) Advances in simulation

    Google Scholar 

  38. Ames WF (1977) Numerical methods for partial differential equations. Academic, New York

    Google Scholar 

  39. Hurlebaus J, Buchholz A, Alt W et al (2002) MMT – a pathway modeling tool for data from rapid sampling experiments. In Silico Biol 2:467–484

    CAS  Google Scholar 

  40. Griewank A, Walther A (2009) Evaluating derivatives: principles and techniques of algorithmic differentiation. SIAM, PA

    Google Scholar 

  41. Elsheikh A, Wiechert W (2008) Automatic sensitivity analysis of DAE-systems generated from equation-based modeling languages. Advances in automatic differentiation. Lecture notes in computational science and engineering, vol 64. Springer, Berlin

    Google Scholar 

  42. Elsheikh A, Noack S, Wiechert W (2008) Sensitivity analysis of Modelica applications via automatic differentiation. In: 6th international Modelica conference, Bielefeld, Germany

    Google Scholar 

  43. Bernhard P (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, New York

    Google Scholar 

  44. Modelica and the Modelica Association. www.modelica.org

  45. The OpenModelica Project. www.ida.liu.se/projects/OpenModelica

  46. Nilsson EL, Fritzson P (2005) Biochemical and metabolic modeling and simulation with Modelica. In: BioMedSim. Linköping, Sweden

    Google Scholar 

  47. Bornstein BJ, Keating SM, Jouraku A et al (2008) LibSBML: an API library for SBML. Bioinformatics 24:880–881

    Article  CAS  Google Scholar 

Download references

Acknowledgment

This work was funded by German Ministry of Education and Research (BMBF) within the SysMAP Project (0313704).

Author information

Authors and Affiliations

  1. Institut für Biotechnologie 2, Forschungszentrum Jülich, 52428, Jülich, Germany

    Wolfgang Wiechert, Stephan Noack & Atya Elsheikh

Authors
  1. Wolfgang Wiechert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Noack
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atya Elsheikh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wolfgang Wiechert .

Editor information

Editors and Affiliations

  1. Inst. Bioverfahrenstechnik, TU Braunschweig, Gaußstraße 17, Braunschweig, 38106, Germany

    Christoph Wittmann

  2. Inst. Bioverfahrenstechnik, TU Braunschweig, Gaußstraße 17, Braunschweig, 38106, Germany

    Rainer Krull

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer

About this chapter

Cite this chapter

Wiechert, W., Noack, S., Elsheikh, A. (2009). Modeling Languages for Biochemical Network Simulation: Reaction vs Equation Based Approaches. In: Wittmann, C., Krull, R. (eds) Biosystems Engineering II. Advances in Biochemical Engineering / Biotechnology, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10_2009_64

Download citation

Publish with us

Policies and ethics

Search

Navigation