Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/64370
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dc.titleParameter identifiability of power-law biochemical system models
dc.contributor.authorSrinath, S.
dc.contributor.authorGunawan, R.
dc.date.accessioned2014-06-17T07:46:26Z
dc.date.available2014-06-17T07:46:26Z
dc.date.issued2010-09
dc.identifier.citationSrinath, S., Gunawan, R. (2010-09). Parameter identifiability of power-law biochemical system models. Journal of Biotechnology 149 (3) : 132-140. ScholarBank@NUS Repository.
dc.identifier.issn01681656
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/64370
dc.description.abstractMathematical modeling has become an integral component in biotechnology, in which these models are frequently used to design and optimize bioprocesses. Canonical models, like power-laws within the Biochemical Systems Theory, offer numerous mathematical and numerical advantages, including built-in flexibility to simulate general nonlinear behavior. The construction of such models relies on the estimation of unknown case-specific model parameters by way of experimental data fitting, also known as inverse modeling. Despite the large number of publications on this topic, this task remains the bottleneck in canonical modeling of biochemical systems. The focus of this paper concerns with the question of identifiability of power-law models from dynamic data, that is, whether the parameter values can be uniquely and accurately identified from time-series data. Existing and newly developed parameter identifiability methods were applied to two power-law models of biochemical systems, and the results pointed to the lack of parametric identifiability as the root cause of the difficulty faced in the inverse modeling. Despite the focus on power-law models, the analyses and conclusions are extendable to other canonical models, and the issue of parameter identifiability is expected to be a common problem in biochemical system modeling. © 2010 Elsevier B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jbiotec.2010.02.019
dc.sourceScopus
dc.subjectBiochemical Systems Theory
dc.subjectConfidence region
dc.subjectIdentifiability analysis
dc.subjectInverse modeling
dc.subjectPower-law models
dc.typeArticle
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.sourcetitleJournal of Biotechnology
dc.description.volume149
dc.description.issue3
dc.description.page132-140
dc.description.codenJBITD
dc.identifier.isiut000281985100006
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