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GLOBAL A PRIORI IDENTIFIABILITY OF MODELS OF FLOW-CELL OPTICAL BIOSENSOR EXPERIMENTS

Part of: Physiological, cellular and medical topics Controllability, observability, and system structure Algorithms - Computer Science Systems theory and control: General Control systems

Published online by Cambridge University Press:  14 June 2018

JASON M. WHYTE
JASON M. WHYTE*
Affiliation:
MASCOS and ACEMS, School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia email jason.whyte@unimelb.edu.au
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Abstract

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Keywords

linear switching systemsglobal a priori identifiabilitycompartmental modellingsystems theorysymbolic algebrabiosensorsbiochemical kineticskinetic experiments

MSC classification

Primary: 93C30: Systems governed by functional relations other than differential equations (such as hybrid and switching systems)
Secondary: 93A30: Mathematical modeling (models of systems, model-matching, etc.) 68W30: Symbolic computation and algebraic computation 92C45: Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) 93B99: None of the above, but in this section
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 98 , Issue 2 , October 2018 , pp. 350 - 352
Copyright
© 2018 Australian Mathematical Publishing Association Inc. 

Footnotes

Thesis submitted to the University of Melbourne in December 2016; examiners’ reports received June 2017; degree approved on 2 August 2017; original supervisor Charles Pearce, supervisors Anthony Guttman and Peter Taylor. The research towards this thesis was supported by an Australian Postgraduate Award and a MASCOS postgraduate scholarship.

References

Whyte, J. M., ‘Inferring global a priori identifiability of optical biosensor experiment models’, in: 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, 2013 (IEEE, Piscataway, NJ, 2013), 17–22, doi:10.1109/BIBM.2013.6732453.Google Scholar
Whyte, J. M., ‘The practical value of an explicit response model in quantitative uses of Biacore™ biosensors’, Preprint, 2018, doi:10.1101/294843.Google Scholar