Biology – Quantitative Biology – Quantitative Methods
Scientific paper
2007-02-28
Biology
Quantitative Biology
Quantitative Methods
Scientific paper
The switch-like character of gene regulation has motivated the use of hybrid, discrete-continuous models of genetic regulatory networks. While powerful techniques for the analysis, verification, and control of hybrid systems have been developed, the specificities of the biological application domain pose a number of challenges, notably the absence of quantitative information on parameter values and the size and complexity of networks of biological interest. We introduce a method for the analysis of reachability properties of genetic regulatory networks that is based on a class of discontinuous piecewise-affine (PA) differential equations well-adapted to the above constraints. More specifically, we introduce a hyperrectangular partition of the state space that forms the basis for a discrete abstraction preserving the sign of the derivatives of the state variables. The resulting discrete transition system provides a conservative approximation of the qualitative dynamics of the network and can be efficiently computed in a symbolic manner from inequality constraints on the parameters. The method has been implemented in the computer tool Genetic Network Analyzer (GNA), which has been applied to the analysis of a regulatory system whose functioning is not well-understood by biologists, the nutritional stress response in the bacterium Escherichia coli.
Batt Grégory
Geiselmann Johannes
Jong Hidde de
Page Michel
Ropers Delphine
No associations
LandOfFree
Symbolic Reachability Analysis of Genetic Regulatory Networks using Qualitative Abstractions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Symbolic Reachability Analysis of Genetic Regulatory Networks using Qualitative Abstractions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Symbolic Reachability Analysis of Genetic Regulatory Networks using Qualitative Abstractions will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-525194