Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-03845-7_17
Title: Probabilistic approximations of signaling pathway dynamics
Authors: Liu, B. 
Thiagarajan, P.S. 
Hsu, D. 
Issue Date: 2009
Source: Liu, B.,Thiagarajan, P.S.,Hsu, D. (2009). Probabilistic approximations of signaling pathway dynamics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5688 LNBI : 251-265. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-03845-7_17
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. © 2009 Springer Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40349
ISBN: 3642038441
ISSN: 03029743
DOI: 10.1007/978-3-642-03845-7_17
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