Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.tcs.2011.01.021
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dc.titleProbabilistic approximations of ODEs based bio-pathway dynamics
dc.contributor.authorLiu, B.
dc.contributor.authorHsu, D.
dc.contributor.authorThiagarajan, P.S.
dc.date.accessioned2013-07-04T07:40:20Z
dc.date.available2013-07-04T07:40:20Z
dc.date.issued2011
dc.identifier.citationLiu, B., Hsu, D., Thiagarajan, P.S. (2011). Probabilistic approximations of ODEs based bio-pathway dynamics. Theoretical Computer Science 412 (21) : 2188-2206. ScholarBank@NUS Repository. https://doi.org/10.1016/j.tcs.2011.01.021
dc.identifier.issn03043975
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39380
dc.description.abstractBio-chemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we propose a discrete probabilistic approximation of the ODEs dynamics. We do so by discretizing the value and the time domain and assuming a distribution of initial states w.r.t. the discretization. Then we sample a representative set of initial states according to the assumed initial distribution and generate a corresponding set of trajectories through numerical simulations. Finally, using the structure of the signaling pathway we encode these trajectories compactly as a dynamic Bayesian network. This approximation of the signaling pathway dynamics has several advantages. First, the discretized nature of the approximation helps to bridge the gap between the accuracy of the results obtained by ODE simulation and the limited precision of experimental data used for model construction and verification. Second and more importantly, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques instead of resorting to a large number of ODE simulations. We have tested our method on ODE models of the EGF-NGF signaling pathway [1] and the segmentation clock pathway [2]. The results are very promising in terms of accuracy and efficiency. © 2011 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.tcs.2011.01.021
dc.sourceScopus
dc.subjectComputational systems biology
dc.subjectDynamic Bayesian networks
dc.subjectModeling
dc.subjectODEs
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.tcs.2011.01.021
dc.description.sourcetitleTheoretical Computer Science
dc.description.volume412
dc.description.issue21
dc.description.page2188-2206
dc.description.codenTCSCD
dc.identifier.isiut000290078000008
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