Simulation and optimization tools to study design principles of biological networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006. === Includes bibliographical references. === Recent studies have developed preliminary wiring diagrams for a number of important biological networks. However, the design principles governing the constructi...

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Main Author: Adiwijaya, Bambang Senoaji
Other Authors: Bruce Tidor and Paul I. Barton.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2007
Subjects:
Online Access:http://hdl.handle.net/1721.1/37973
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spelling ndltd-MIT-oai-dspace.mit.edu-1721.1-379732019-05-02T16:25:10Z Simulation and optimization tools to study design principles of biological networks Adiwijaya, Bambang Senoaji Bruce Tidor and Paul I. Barton. Massachusetts Institute of Technology. Biological Engineering Division. Massachusetts Institute of Technology. Biological Engineering Division. Biological Engineering Division. Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006. Includes bibliographical references. 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. by Bambang Senoaji Adiwijaya. Ph.D. 2007-07-18T13:19:08Z 2007-07-18T13:19:08Z 2006 2006 Thesis http://hdl.handle.net/1721.1/37973 146092400 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 146 leaves application/pdf Massachusetts Institute of Technology
collection NDLTD
language English
format Others
sources NDLTD
topic Biological Engineering Division.
spellingShingle Biological Engineering Division.
Adiwijaya, Bambang Senoaji
Simulation and optimization tools to study design principles of biological networks
description Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006. === Includes bibliographical references. === 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. === by Bambang Senoaji Adiwijaya. === Ph.D.
author2 Bruce Tidor and Paul I. Barton.
author_facet Bruce Tidor and Paul I. Barton.
Adiwijaya, Bambang Senoaji
author Adiwijaya, Bambang Senoaji
author_sort Adiwijaya, Bambang Senoaji
title Simulation and optimization tools to study design principles of biological networks
title_short Simulation and optimization tools to study design principles of biological networks
title_full Simulation and optimization tools to study design principles of biological networks
title_fullStr Simulation and optimization tools to study design principles of biological networks
title_full_unstemmed Simulation and optimization tools to study design principles of biological networks
title_sort simulation and optimization tools to study design principles of biological networks
publisher Massachusetts Institute of Technology
publishDate 2007
url http://hdl.handle.net/1721.1/37973
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