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On Identification of Biological Systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

System identification finds nowadays application in various areas of biological research as a tool of empiric mathematical modeling and model individualization. A fundamental challenge of system identification in biology awaits in the form of response variability. Furthermore, biological systems tend to exhibit high degree of nonlinearity as well as significant time delays. This thesis covers system identification approaches developed for the applications within two particular biomedical fields: neuroscience and endocrinology.

The first topic of the thesis is parameter estimation of the classical Elementary Motion Detector (EMD) model in insect vision. There are two important aspects to be taken care of in the identification approach, namely the nonlinear dynamics of the individual EMD and the spatially distributed structure of multiple detectors producing a measurable neural response. Hence, the suggested identification method is comprised of two consecutive stages addressing each of the above aspects. Furthermore, visual stimulus design for high spatial excitation order has been investigated.

The second topic is parameter estimation of mathematical model for testosterone regulation in the human male. The main challenges of this application are in the unavailability of input signal measurements and the presence of an unknown pulsatile feedback in the system resulting in a highly nonlinear closed-loop dynamics. Semi-blind identification method has been developed based on a recently proposed pulse-modulated model of pulsatile endocrine regulation.

The two system identification problems treated in the thesis bear some resemblance in the sense that both involve measured signals that can be seen as square-integrable functions of time. This property is handled by transforming the signals into the Laguerre domain, i.e. by equivalently representing the functions with their infinite Laguerre series.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. , 40 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1117
Keyword [en]
system identification, biomedical systems, insect vision, endocrine systems, orthogonal basis functions, time delay, impulse response, Laguerre functions, Laguerre polynomials, excitation design
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
URN: urn:nbn:se:uu:diva-215699ISBN: 978-91-554-8857-4 (print)OAI: oai:DiVA.org:uu-215699DiVA: diva2:688131
Public defence
2014-03-03, Room 2247, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2014-02-07 Created: 2014-01-15 Last updated: 2014-07-21
List of papers
1. Laguerre domain identification of continuous linear time-delay systems from impulse response data
Open this publication in new window or tab >>Laguerre domain identification of continuous linear time-delay systems from impulse response data
2012 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 48, no 11, 2902-2907 p.Article in journal (Refereed) Published
Abstract [en]

An expression for the Laguerre spectrum of the impulse response of a linear continuous time-invariant system with input or output delay is derived. A discrete state-space description of the time-delay system in the Laguerre shift operator is obtained opening up for the use of conventional identification techniques. A method for Laguerre domain identification of continuous time-delay systems from impulse response data is then proposed. Linear time-invariant systems resulting from cascading finite-dimensional dynamics with pure time delays are considered. Subspace identification is utilized for estimation of finitedimensional dynamics. An application to blind identification of a mathematical model of an endocrine system with pulsatile regulation is also provided.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-183832 (URN)10.1016/j.automatica.2012.06.077 (DOI)000310717100020 ()
Available from: 2012-07-15 Created: 2012-11-02 Last updated: 2017-12-07Bibliographically approved
2. Identification of a pulsatile endocrine model from hormone concentration data
Open this publication in new window or tab >>Identification of a pulsatile endocrine model from hormone concentration data
2012 (English)In: 2012 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS (CCA), 2012, 356-363 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents two approaches to estimate parameters of a mathematical model of a bipartite endocrine axis. Secretion of one of the involved hormones is stimulated by the concentration of another one, with the latter secreted in a pulsatile manner. The system output can be modeled as the response of a linear time-invariant system to a train of Dirac delta functions with unknown weights and fired at unknown instants. The hormone mechanism in question appears often in animal and human endocrine systems, e. g. in the regulation of testosterone in the human male. The model has been introduced elsewhere and makes use of pulse-modulated feedback for describing pulsatile endocrine regulation. The first identification approach is based on the mathematical machinery of constrained nonlinear least-squares minimization, while the second one is based on Laguerre domain identification of continuous time-delay systems. Both algorithm perform reasonably well on actual biological data yielding accurate fitting of luteinizing hormone concentration profiles.

Series
IEEE International Conference on Control Applications, ISSN 1085-1992
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-206782 (URN)000320336200046 ()978-1-4673-4505-7 (ISBN)
Conference
IEEE International Conference on Control Applications (CCA) part of 6th IEEE Multi-Conference on Systems and Control (IEEE MSC), OCT 03-05, 2012, Dubrovnik, CROATIA
Available from: 2013-09-04 Created: 2013-09-04 Last updated: 2014-02-10Bibliographically approved
3. Laguerre domain modeling of continuous time delay in the face of finite-dimensional perturbation
Open this publication in new window or tab >>Laguerre domain modeling of continuous time delay in the face of finite-dimensional perturbation
2014 (English)Article in journal (Other academic) Submitted
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-215697 (URN)
Available from: 2014-01-15 Created: 2014-01-15 Last updated: 2014-02-14
4. Laguerre Domain Identification of the Elementary Motion Detector Model in Insect Vision
Open this publication in new window or tab >>Laguerre Domain Identification of the Elementary Motion Detector Model in Insect Vision
2013 (English)In: Adaptation and Learning in Control and Signal Processing, International Federation of Automatic Control , 2013, 623-628 p.Conference paper, Published paper (Refereed)
Abstract [en]

A Laguerre domain approach to the identification of the so-called Elementary Motion Detector (EMD) that is hypothesized to constitute the basis of biological motion vision in nearly all animals is proposed. Despite the vast popularity of the EMD concept in both biology and biologically inspired computer vision, the problem of estimating the dynamics of the EMD from experimental data has been poorly addressed. The choice of the Laguerre domain for the representation of the input and the output of the EMD is motivated by the pulse-modulated character of the visual stimuli produced by the CRT displays that are often used in animal experiments. An analytical expression for the Laguerre spectrum of the EMD output given the Laguerre spectrum of the input is derived and a parameter estimation algorithm of the system dynamics is developed. The feasibility of the approach is illustrated by simulation using actual visual stimuli from fly electrophysiology.

Place, publisher, year, edition, pages
International Federation of Automatic Control, 2013
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-211727 (URN)10.3182/20130703-3-FR-4038.00070 (DOI)978-3-902823-37-3 (ISBN)
Conference
11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing
Available from: 2013-11-29 Created: 2013-11-29 Last updated: 2014-02-10Bibliographically approved
5. Identification of the elementary motion detector model in fly motion vision from intracellularly recorded neural data
Open this publication in new window or tab >>Identification of the elementary motion detector model in fly motion vision from intracellularly recorded neural data
2014 (English)Article in journal (Other academic) Submitted
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-215693 (URN)
Available from: 2014-01-15 Created: 2014-01-15 Last updated: 2014-02-14Bibliographically approved
6. Spatial excitation properties of sinusoidal grating stimuli in the identification of a layer of motion detectors
Open this publication in new window or tab >>Spatial excitation properties of sinusoidal grating stimuli in the identification of a layer of motion detectors
2014 (English)Article in journal (Other academic) Submitted
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-215691 (URN)
Available from: 2014-01-15 Created: 2014-01-15 Last updated: 2014-02-14Bibliographically approved

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