A biological simulator using a stochastic approach for synthetic biology
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. === Includes bibliographical references (leaves 58-59). === Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a n...
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ndltd-MIT-oai-dspace.mit.edu-1721.1-333072019-05-02T15:37:05Z A biological simulator using a stochastic approach for synthetic biology Kim, Daniel D., 1982- Thomas F. Knight, Jr. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. Includes bibliographical references (leaves 58-59). Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell. by Daniel D. Kim. M.Eng. 2006-07-13T15:14:07Z 2006-07-13T15:14:07Z 2005 2005 Thesis http://hdl.handle.net/1721.1/33307 62296074 eng 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 59 leaves 2523967 bytes 2525434 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
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Electrical Engineering and Computer Science. |
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Electrical Engineering and Computer Science. Kim, Daniel D., 1982- A biological simulator using a stochastic approach for synthetic biology |
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. === Includes bibliographical references (leaves 58-59). === Synthetic Biology is a new engineering discipline created by the development of genetic engineering technology. Part of a new engineering discipline is to create new tools to build an integrated engineering environment. In this thesis, I designed and implemented a biological system simulator that will enable synthetic biologists to simulate their systems before they put time into building actual physical cells. Improvements to the current simulators in use include a design that enables extensions in functionality, external input signals, and a GUI that allows user interaction. The significance of the simulation results was tested by comparing them to actual live cellular experiments. The results showed that the new simulator can successfully simulate the trends of a simple synthetic cell. === by Daniel D. Kim. === M.Eng. |
author2 |
Thomas F. Knight, Jr. |
author_facet |
Thomas F. Knight, Jr. Kim, Daniel D., 1982- |
author |
Kim, Daniel D., 1982- |
author_sort |
Kim, Daniel D., 1982- |
title |
A biological simulator using a stochastic approach for synthetic biology |
title_short |
A biological simulator using a stochastic approach for synthetic biology |
title_full |
A biological simulator using a stochastic approach for synthetic biology |
title_fullStr |
A biological simulator using a stochastic approach for synthetic biology |
title_full_unstemmed |
A biological simulator using a stochastic approach for synthetic biology |
title_sort |
biological simulator using a stochastic approach for synthetic biology |
publisher |
Massachusetts Institute of Technology |
publishDate |
2006 |
url |
http://hdl.handle.net/1721.1/33307 |
work_keys_str_mv |
AT kimdanield1982 abiologicalsimulatorusingastochasticapproachforsyntheticbiology AT kimdanield1982 biologicalsimulatorusingastochasticapproachforsyntheticbiology |
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1719024852867743744 |