Probabilistic Approximations of Signaling Pathway Dynamics

  • Authors:

  • Bing Liu;P. S. Thiagarajan;David Hsu

  • Affiliations:

  • NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore,;NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, and Department of Computer Science, National University of Singapore,;NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, and Department of Computer Science, National University of Singapore,

  • Venue:
  • CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
  • Year:
  • 2009

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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.