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|Title:||Probabilistic approximations of signaling pathway dynamics|
|Authors:||Liu, B. |
|Citation:||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  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)|
|Appears in Collections:||Staff Publications|
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