Dräger, Andreas and Schröder, Adrian and Zell, Andreas

Automating mathematical modeling of biochemical reaction networks

In: Systems Biology for Signaling Networks, vol. 1, Springer-Verlag, 2010, pp. 159-205


Abstract

In this chapter we introduce a five-step modeling pipeline that ultimately leads to a mathematical description of a biochemical reaction system. We discuss how to automate each single step and how to put these steps together: First, we have to create a topology of interconversion processes and mutual influences between reactive species. An automated modeling procedure requires an appropriate data format that encodes the model in a computer-readable form. A standard like the Systems Biology Markup Language (SBML) serves this purpose and allows us to add semantic information to each component of the model. Second, from such an annotated network the procedure SBMLsqueezer generates kinetic equations in a context sensitive manner. The resulting model can then be combined with already existing models. Third, we estimate the values of all newly introduced parameters in each created rate law. This procedure requires a time series of quantitative measurements of the reactive species within this system to be available, because we calibrate the parameters with the aim that the model will fit these experimental data. Fourth, an experimental validation of the resulting model is advisable. Finally, a model report should be generated to document the model with all of its components. For a better understanding, we start our consideration with an introduction into necessary standardization attempts in today's systems biology and generalized approaches for common rate equations before we discuss the computer-aided modeling, parameter estimation, and automatic report generation. We complete this chapter with a discussion on possible further improvements of our modeling pipeline.


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BibTeX

@inbook{Draeger2010a,
  chapter = {Automating mathematical modeling of biochemical reaction networks},
  pages = {159--205},
  title = {Systems Biology for Signaling Networks},
  publisher = {Springer-Verlag},
  year = {2010},
  editor = {Choi, Sangdun},
  author = {Dr\"ager, Andreas and Schr\"oder, Adrian and Zell, Andreas},
  volume = {1},
  series = {Systems Biology},
  month = jul,
  abstract = {In this chapter we introduce a five-step modeling pipeline that ultimately
	leads to a mathematical description of a biochemical reaction system.
	We discuss how to automate each single step and how to put these
	steps together: First, we have to create a topology of interconversion
	processes and mutual influences between reactive species. An automated
	modeling procedure requires an appropriate data format that encodes
	the model in a computer-readable form. A standard like the Systems
	Biology Markup Language (SBML) serves this purpose and allows us
	to add semantic information to each component of the model. Second,
	from such an annotated network the procedure SBMLsqueezer generates
	kinetic equations in a context sensitive manner. The resulting model
	can then be combined with already existing models. Third, we estimate
	the values of all newly introduced parameters in each created rate
	law. This procedure requires a time series of quantitative measurements
	of the reactive species within this system to be available, because
	we calibrate the parameters with the aim that the model will fit
	these experimental data. Fourth, an experimental validation of the
	resulting model is advisable. Finally, a model report should be generated
	to document the model with all of its components. For a better understanding,
	we start our consideration with an introduction into necessary standardization
	attempts in today's systems biology and generalized approaches for
	common rate equations before we discuss the computer-aided modeling,
	parameter estimation, and automatic report generation. We complete
	this chapter with a discussion on possible further improvements of
	our modeling pipeline.},
  doi = {10.1007/978-1-4419-5797-9_7},
  url = {http://www.springerlink.com/content/n77k80h76vj17806}
}