Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/35227
DC FieldValue
dc.titleMODEL IDENTIFICATION IN THE BIOCHEMICAL SYSTEMS THEORY
dc.contributor.authorSRIDHARAN SRINATH
dc.date.accessioned2012-10-31T18:01:16Z
dc.date.available2012-10-31T18:01:16Z
dc.date.issued2012-03-27
dc.identifier.citationSRIDHARAN SRINATH (2012-03-27). MODEL IDENTIFICATION IN THE BIOCHEMICAL SYSTEMS THEORY. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/35227
dc.description.abstractMathematical modeling of biological systems using the power-law formalism, the Biochemical Systems Theory (BST) coupled with high-throughput biological measurements transform the model identification into an inverse problem of estimating model parameters from experimental data. Despite the large number of publications on this topic, this task remains the bottleneck in the application of BST modeling in biologically related area. However, many challenges arise from the same underlying problem; incomplete and noisy measurements lack the necessary information in order to accurately estimate the model parameters. This is a parameter identifiability problem. Thus, the focus of my PhD work is parameter-identifiability-centric modeling of biochemical networks using the BST models. The applications of the methods developed for identifiability analysis to two inverse modeling problems within the BST point to the lack of parametric identifiability as the root cause of the difficulty faced in the inverse modeling. Motivated by the results of the analysis to S-systems and GMA models, we developed methods based on nonlinear regression to test the identifiability of decoupled and lin-log systems. The next work deals with design of experiments (DOE) to generate information-rich data that improves parameter identifiability. To this end a multiobjective optimization design criterion that accounts for curvature effects was developed. The final goal of my PhD project was to integrate the tasks of identifiability analysis, DOE and parameter estimation together into a useful MATLAB tool.
dc.language.isoen
dc.subjectMathematical Modeling, Identifiability analysis, Biochemical Systems Theory, Design of Experiments, Multi-objective optimization, Inverse Modeling
dc.typeThesis
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.contributor.supervisorSAIF ABDUL KADIR KHAN
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Ph.D Theses (Open)

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Thesis_Final.pdf3.14 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

208
checked on May 23, 2019

Download(s)

58
checked on May 23, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.