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Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology

Hsiao, Victoria and Swaminathan, Anandh and Murray, Richard M. (2018) Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology. IEEE Control Systems Magazine, 38 (3). pp. 32-62. ISSN 0272-1708. https://resolver.caltech.edu/CaltechAUTHORS:20180524-091716800

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Abstract

Living organisms are differentiated by their genetic material-millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding. However, as DNA sequencing technology has become affordable, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature. In doing so, they test the limits of both natural and engineered organisms.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/MCS.2018.2810459DOIArticle
ORCID:
AuthorORCID
Hsiao, Victoria0000-0001-9297-1522
Swaminathan, Anandh0000-0001-9935-6530
Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2018 IEEE. V. Hsiao and A. Swaminathan contributed equally to this work.
Issue or Number:3
Record Number:CaltechAUTHORS:20180524-091716800
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180524-091716800
Official Citation:V. Hsiao, A. Swaminathan and R. M. Murray, "Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology," in IEEE Control Systems, vol. 38, no. 3, pp. 32-62, June 2018. doi: 10.1109/MCS.2018.2810459
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:86582
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 May 2018 16:24
Last Modified:03 Oct 2019 19:45

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