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Open Access Software

RMBNToolbox: random models for biochemical networks

Tommi Aho1*, Olli-Pekka Smolander1, Jari Niemi12 and Olli Yli-Harja1

Author affiliations

1 Department of Information Technology, Institute of Signal Processing, Tampere University of Technology, Tampere, Finland

2 Department of Information Technology, Institute of Mathematics, Tampere University of Technology, Tampere, Finland

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Citation and License

BMC Systems Biology 2007, 1:22  doi:10.1186/1752-0509-1-22

Published: 24 May 2007

Abstract

Background

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.

Results

We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.

Conclusion

While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.