Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ces.2011.07.003
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dc.titleApplication of scaling and sensitivity analysis for tumor-immune model reduction
dc.contributor.authorLakshmi Kiran, K.
dc.contributor.authorLakshminarayanan, S.
dc.date.accessioned2014-06-17T07:36:14Z
dc.date.available2014-06-17T07:36:14Z
dc.date.issued2011-11-01
dc.identifier.citationLakshmi Kiran, K., Lakshminarayanan, S. (2011-11-01). Application of scaling and sensitivity analysis for tumor-immune model reduction. Chemical Engineering Science 66 (21) : 5164-5172. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ces.2011.07.003
dc.identifier.issn00092509
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/63504
dc.description.abstractMathematical modeling plays a facilitating role in comprehending tumor growth and its interaction with the immune system. However, there are some hindrances in modeling these phenomena. Firstly, the complexity of the tumor-immune model increases with the inclusion of dynamics of different types of immune cells. In this work, complexity is considered in terms of number of parameters and differences in the order of magnitude of their values. We have assumed that the model structure accurately represents the actual tumor-immune interactions. This may result in non-identifiability, imprecise measurement/estimation of the parameters. Secondly, very few parameters in the model will significantly influence the evolution of state variables and it is important for us to know these sensitive parameters.In this work, a recent and elaborate tumor-immune model is considered and its reduced parametric representation is obtained through a systematic scaling approach without any loss in its predictive ability. The advantage of scaling approach is quantified using theoretical identifiability analysis and by evaluating the condition number of the Fisher information matrix. Then, global sensitivity analysis is applied on the reduced model to identify the key parameters affecting the tumor progression. Such model reduction and parameter analysis may be necessary in order to increase the possibility of bringing model based approaches to standard medical practice and patient care. © 2011 Elsevier Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.ces.2011.07.003
dc.sourceScopus
dc.subjectMathematical modeling
dc.subjectModel reduction
dc.subjectNon-linear dynamics
dc.subjectParameter identification
dc.subjectScaling analysis
dc.subjectSensitivity analysis
dc.typeArticle
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1016/j.ces.2011.07.003
dc.description.sourcetitleChemical Engineering Science
dc.description.volume66
dc.description.issue21
dc.description.page5164-5172
dc.description.codenCESCA
dc.identifier.isiut000294205800019
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