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Probabilistic Approximations of Signaling Pathway Dynamics

  • Bing Liu
  • P. S. Thiagarajan
  • David Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5688)

Abstract

Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic approximation of the ODE dynamics by discretizing the value space and the time domain. We then sample a representative set of trajectories and exploit the discretization and the structure of the signaling pathway to encode these trajectories compactly as a dynamic Bayesian network. As a result, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques. We have tested our method on a model of EGF-NGF signaling pathway [1] and the results are very promising in terms of both accuracy and efficiency.

Keywords

Model Check Probabilistic Approximation Latin Hypercube Sampling Dynamic Bayesian Network Global Sensitivity Analysis 
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-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bing Liu
    • 1
  • P. S. Thiagarajan
    • 1
    • 2
  • David Hsu
    • 1
    • 2
  1. 1.NUS Graduate School for Integrative Sciences and EngineeringNational University of SingaporeSingapore
  2. 2.Department of Computer ScienceNational University of SingaporeSingapore

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