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Emilio Frazzoli. Norris, Noele Rosalie Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-02-10T13:33:41Z 2014-02-10T13:33:41Z 2013 http://hdl.handle.net/1721.1/84722 868904374 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 109-113). While many species of bacteria are motile, they use various random strategies to determine where to swim. This chemotaxis allow bacterial populations to distribute themselves in accordance to distributions of nutrients found within an environment. We extend past work describing a chemotactic E. coli cell as an ergodic, stochastic hybrid system and use experimental data on bacterial motion in microfluidic environments to model other species of bacteria. Our focus is on understanding the differences between the run-and-tumble strategy of E. coli and the more complicated run-reverse-flick strategy of the marine bacterium Vibrio alginolyticus. We use stochastic stability theory to analyze the chemotaxis models in terms of their stationary distributions and also derive a diffusion approximation of the system that provides further insight into the performance of various strategies. By comparing general chemotactic strategies, we hypothesize why various strategies may be evolutionarily advantageous for particular environments. These results also provide intuition for designing minimalistic multi-agent robotic systems that can be used for various environmental monitoring and source-seeking tasks. by Noele Rosalie Norris. S.M. 113 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Exploring the optimality of various bacterial motility strategies : a stochastic hybrid systems approach Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="7a23837b-72bb-4325-b218-5ce205b90714"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Exploring the optimality of various bacterial motility strategies : a stochastic hybrid systems approach</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Norris, Noele Rosalie</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>While many species of bacteria are motile, they use various random strategies to determine where to swim. This chemotaxis allow bacterial populations to distribute themselves in accordance to distributions of nutrients found within an environment. We extend past work describing a chemotactic E. coli cell as an ergodic, stochastic hybrid system and use experimental data on bacterial motion in microfluidic environments to model other species of bacteria. Our focus is on understanding the differences between the run-and-tumble strategy of E. coli and the more complicated run-reverse-flick strategy of the marine bacterium Vibrio alginolyticus. We use stochastic stability theory to analyze the chemotaxis models in terms of their stationary distributions and also derive a diffusion approximation of the system that provides further insight into the performance of various strategies. By comparing general chemotactic strategies, we hypothesize why various strategies may be evolutionarily advantageous for particular environments. These results also provide intuition for designing minimalistic multi-agent robotic systems that can be used for various environmental monitoring and source-seeking tasks.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>