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Model Checking Genetic Regulatory Networks with Parameter Uncertainty

  • Grégory Batt
  • Calin Belta
  • Ron Weiss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4416)

Abstract

The lack of precise numerical information for the values of biological parameters severely limits the development and analysis of models of genetic regulatory networks. To deal with this problem, we propose a method for the analysis of genetic regulatory networks with parameter uncertainty. We consider models based on piecewise-multiaffine differential equations, dynamical properties expressed in temporal logic, and intervals for the values of uncertain parameters. The problem is then either to guarantee that the system satisfies the expected properties for every possible parameter value - the corresponding parameter set is then called valid - or to find valid subsets of a given parameter set. The proposed method uses discrete abstractions and model checking, and allows for efficient search of the parameter space. This approach has been implemented in a tool for robust verification of gene networks (RoVerGeNe) and applied to the tuning of a synthetic network build in E. coli.

Keywords

Transition System Parameter Uncertainty Synthetic Biology Uncertain Parameter Linear Temporal Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Grégory Batt
    • 1
  • Calin Belta
    • 1
  • Ron Weiss
    • 2
  1. 1.Center for Information and Systems Engineering and Center for BioDynamics, Boston University, Brookline, MAUSA
  2. 2.Department of Electrical Engineering and Department of Molecular Biology, Princeton University, Princeton, NJUSA

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