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Simulation and optimization tools to study design principles of biological networks

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dc.contributor.advisor Bruce Tidor and Paul I. Barton. en_US
dc.contributor.author Adiwijaya, Bambang Senoaji en_US
dc.contributor.other Massachusetts Institute of Technology. Biological Engineering Division. en_US
dc.date.accessioned 2007-07-18T13:19:08Z
dc.date.available 2007-07-18T13:19:08Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/37973
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006. en_US
dc.description Includes bibliographical references. en_US
dc.description.abstract Recent studies have developed preliminary wiring diagrams for a number of important biological networks. However, the design principles governing the construction and operation of these networks remain mostly unknown. To discover design principles in these networks, we investigated and developed a set of computational tools described below. First, we looked into the application of optimization techniques to explore network topology, parameterization, or both, and to evaluate relative fitness of networks operational strategies. In particular, we studied the ability of an enzymatic cycle to produce dynamic properties such as responsiveness and transient noise filtering. We discovered that non-linearity of the enzymatic cycle allows more effective filtering of transient noise. Furthermore, we found that networks with multiple activation steps, despite being less responsive, are better in filtering transient noise. Second, we explored a method to construct compact models of signal transduction networks based on a protein-domain network representation. This method generates models whose number of species, in the worst case, scales quadratically to the number of protein-domain sites and modification states, a tremendous saving over the combinatorial scaling in the more standard mass-action model was estimated to consist of more that 10⁷ species and was too large to simulate; however, a simplified model consists of only 132 state variables and produced intuitive behavior. The resulting models were utilized to study the roles of a scaffold protein and of a shared binding domain to pathway functions. en_US
dc.description.statementofresponsibility by Bambang Senoaji Adiwijaya. en_US
dc.format.extent 146 leaves en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Biological Engineering Division. en_US
dc.title Simulation and optimization tools to study design principles of biological networks en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Biological Engineering Division. en_US
dc.identifier.oclc 146092400 en_US


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