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dc.contributor.advisorAlexander van Oudenaarden and Sebastian Seung.en_US
dc.contributor.authorThattai, Mukund, 1976-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Physics.en_US
dc.date.accessioned2005-10-14T20:39:59Z
dc.date.available2005-10-14T20:39:59Z
dc.date.copyright2004en_US
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/29458
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2004.en_US
dc.descriptionIncludes bibliographical references (p. 133-139).en_US
dc.description.abstractLiving cells are made up of networks of interacting genes, proteins and biochemicals. Simple interactions between network components can lead to complex collective dynamics. Cells use these emergent dynamical properties to propagate signals and perform computations, to probe their surroundings and generate appropriate responses. These phenomena can only be understood at the network level, not at the level of individual components. We have explored the behavior of a variety of biological networks, using quantitative experimental measurements to validate the predictions of detailed network models. Signal and noise in genetic networks. Biochemical reactions often involve small numbers of molecules, and are therefore dominated by stochastic fluctuations. We theoretically analyzed the origin of fluctuations in linearized genetic networks, and showed that these fluctuations could be suppressed by certain network architectures. We then experimentally measured the expression of a reporter protein in single cells of an isogenic bacterial population, and found that stochastic mechanisms caused expression levels to vary from cell to cell. We demonstrated that this variability arose due to number fluctuations in transcription and translation processes, and showed that it could be regulated using genetic parameters. Computational properties of genetic networks. Regulatory networks implement intricate computations which determine the response of a cell to external stimuli. The phosphotransferase system of Escherichia coli regulates the metabolism of several sugars. We used a biochemical model of the system to investigate the range of possible metabolic responses it could generate.en_US
dc.description.abstract(cont.) We then experimentally investigated a simpler sugar uptake system, the lactose utilization network of Escherichia coli. Expression at the lac operon is regulated by a positive feedback loop, causing the system to have a hysteretic bistable response. We used a reporter protein to monitor lac expression in single cells, and mapped the occurrence of bistable behavior as a function of external sugar concentrations. The structure of this phase diagram allowed us to extract detailed information about network architecture. This system provides a simple example of cellular memory and serves as a model for cell differentiation, showing how transient external stimuli can be used to generate sustained internal responses.en_US
dc.description.statementofresponsibilityby Mukund Thattai.en_US
dc.format.extent139 p.en_US
dc.format.extent5575608 bytes
dc.format.extent9701840 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectPhysics.en_US
dc.titleThe dynamics of genetic networksen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Physics.en_US
dc.identifier.oclc56216889en_US


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