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University of Michigan

1. Inamdar, Munish Vishwas. Mobile traps, targets and probabilistic detection: Analysis and simulations of stochastic, biological sensing.

Degree: PhD, Mechanical engineering, 2006, University of Michigan

The association of two particles upon collision is an apparently simple problem in kinetics, and many other physical phenomena. Treatments of binding events, from atomistic to astronomical phenomena, use appropriate physics assumptions generally requiring the relative speeds, masses and binding properties of the interacting species. Simple diffusion is used to map many fluxes of species that bind and break free of one another in domains both homogeneous and complex. In this thesis, two challenge problems in biology, at rather different scales, are investigated for their value both in scientific terms, and in terms of evaluating statistical techniques, rather than a simple diffusion approach, to interrogate several critical phenomena. The first of these is the transport of zinc (Chapter 2); the second is the fertilization of eggs in a free-spawning invertebrate (Chapter 3). Though these problems pose different physical and biochemical conditions, a unifying set of statistical techniques can be used to investigate each (Chapter 4), following the classical Bayesian statistics, updated here to include more recent solutions in cluster statistics. To understand the particle level uptake of highly reactive, but tightly regulated zinc ions by protein molecules, a mobile trap mobile target algorithm was developed and implemented to obtain particle level interactions probabilities, p1 and p2 that were forward map to the kinetic constants of the reaction kon and koff. Using two-fold approach of experimentation and stochastic simulations, we also investigated the chemotactic role of the jelly coats around Arbacia punctulata eggs. Stochastic simulations were performed using diffusion coefficients obtained experimentally in with a microfluidic device designed for this purpose. The final element of this thesis was to formalize a Bayesian treatment of these two classes of problems: one in which both traps and targets were mobile, and one in which traps were static, but targets were mobile. We applied Bayesian statistics to zinc ion sensing to estimate the number of sensors required to measure low ion concentrations and demonstrated the effectiveness of percolating sensors. We also developed more general guidelines for the assumptions that must be made in modeling statistical binding events. Advisors/Committee Members: Sastry, Ann Marie (advisor), Lastoskie, Christian M. (advisor).

Subjects/Keywords: Analysis; Arbacia Punctulata; Biological Sensing; Mobile Traps; Probabilistic Detection; Simulations; Stochastic; Targets

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Inamdar, M. V. (2006). Mobile traps, targets and probabilistic detection: Analysis and simulations of stochastic, biological sensing. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/126059

Chicago Manual of Style (16th Edition):

Inamdar, Munish Vishwas. “Mobile traps, targets and probabilistic detection: Analysis and simulations of stochastic, biological sensing.” 2006. Doctoral Dissertation, University of Michigan. Accessed April 18, 2021. http://hdl.handle.net/2027.42/126059.

MLA Handbook (7th Edition):

Inamdar, Munish Vishwas. “Mobile traps, targets and probabilistic detection: Analysis and simulations of stochastic, biological sensing.” 2006. Web. 18 Apr 2021.

Vancouver:

Inamdar MV. Mobile traps, targets and probabilistic detection: Analysis and simulations of stochastic, biological sensing. [Internet] [Doctoral dissertation]. University of Michigan; 2006. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2027.42/126059.

Council of Science Editors:

Inamdar MV. Mobile traps, targets and probabilistic detection: Analysis and simulations of stochastic, biological sensing. [Doctoral Dissertation]. University of Michigan; 2006. Available from: http://hdl.handle.net/2027.42/126059

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