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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Probabilistic Approximations of Signaling Pathway Dynamics
verfasst von : Bing Liu, P. S. Thiagarajan, David Hsu
Erschienen in: Computational Methods in Systems Biology
Verlag: Springer Berlin Heidelberg
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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.