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Journal Article
Modelling and analysis of gene regulatory networks
2008
Overview
Gene regulatory networks control many cellular processes such as cell cycle, cell differentiation, metabolism and signal transduction. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
Key Points
By combining biological knowledge and experimental data with judicious computational modelling, regulatory networks can be dissected, and analysis results can shed light on life and disease mechanisms.
Logical models provide a simplified, yet useful, approach that copes well with partial knowledge. They have been used successfully to identify specific regulatory interactions.
Continuous models can describe a wide range of phenomena and can be readily compared to experimental measurements.
Single-molecule level models simulate network behaviour at the resolution of individual molecular interactions. They have been successfully applied to regulatory networks that exhibit stochastic behaviour.
The advantages and disadvantages of the different approaches are discussed, along with future goals of computational modelling.
Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
Publisher
Nature Publishing Group UK,Nature Publishing Group
