Analysis of single-molecule kinesin assay data by hidden Markov model filtering

Persistent Link:
http://hdl.handle.net/10150/280221
Title:
Analysis of single-molecule kinesin assay data by hidden Markov model filtering
Author:
Walton, David Brian
Issue Date:
2002
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Observations of the position of a microscopic bead attached to a single kinesin protein moving along a microtubule contains detailed information about the position of the kinesin as a function of time, although this information remains obscured because of the fluctuations of the bead. The theory of hidden Markov models suggests a possible theoretical framework to analyze these data with an explicit stochastic model describing the kinesin cycle and the attached bead. We model the mechanical cycle of kinesin using a discrete time Markov chain on a periodic lattice, representing the microtubule, and model the position of the bead using an Ornstein-Uhlenbeck autoregressive process. We adapt the standard machinery of hidden Markov models to derive the likelihood of this model using a reference measure, and use the Expectation-Maximization (EM) algorithm to estimate model parameters. Simulated data sets indicate that the method does have potential to better analyze kinesin-bead experiments. However, analysis of the experimental data of Visscher et al. (1999) indicates that current data sets still lack the time resolution to extract significant information about intermediate states. Considerations for future experimental designs are suggested to allow better hidden Markov model analysis.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Mathematics.; Biophysics, General.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Applied Mathematics
Degree Grantor:
University of Arizona
Advisor:
Watkins, Joseph C.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAnalysis of single-molecule kinesin assay data by hidden Markov model filteringen_US
dc.creatorWalton, David Brianen_US
dc.contributor.authorWalton, David Brianen_US
dc.date.issued2002en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractObservations of the position of a microscopic bead attached to a single kinesin protein moving along a microtubule contains detailed information about the position of the kinesin as a function of time, although this information remains obscured because of the fluctuations of the bead. The theory of hidden Markov models suggests a possible theoretical framework to analyze these data with an explicit stochastic model describing the kinesin cycle and the attached bead. We model the mechanical cycle of kinesin using a discrete time Markov chain on a periodic lattice, representing the microtubule, and model the position of the bead using an Ornstein-Uhlenbeck autoregressive process. We adapt the standard machinery of hidden Markov models to derive the likelihood of this model using a reference measure, and use the Expectation-Maximization (EM) algorithm to estimate model parameters. Simulated data sets indicate that the method does have potential to better analyze kinesin-bead experiments. However, analysis of the experimental data of Visscher et al. (1999) indicates that current data sets still lack the time resolution to extract significant information about intermediate states. Considerations for future experimental designs are suggested to allow better hidden Markov model analysis.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectMathematics.en_US
dc.subjectBiophysics, General.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineApplied Mathematicsen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorWatkins, Joseph C.en_US
dc.identifier.proquest3073272en_US
dc.identifier.bibrecord.b43479054en_US
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