RMBNToolbox: random models for biochemical networks

Tommi Aho,Olli-Pekka Smolander,Olli Yli-Harja,Jari Niemi

doi:10.1186/1752-0509-1-22

Abstract

BackgroundThere 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.ResultsWe 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.ConclusionWhile 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.

Highlights

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models
The example demonstrates how metabolic fluxes in a steady state can be decomposed by elementary flux modes
We have presented a software called RMBNToolbox that can be used to generate random models for biochemical networks

Summary

Introduction

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. 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. Modeling and analysis of large biochemical networks is in its infancy. In the field of computational systems biology, researchers modeling these networks often aim at predicting the system behavior in response to a given treatment. The structures of various biochemical networks are under extensive research. Best known are the structures of metabolic networks which are reconstructed on the basis of genome annotation, and biochemical and physi-

Results

Discussion

Conclusion