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Control and information-theoretic an...

The Johns Hopkins University.

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Control and information-theoretic analysis of biological signaling systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Control and information-theoretic analysis of biological signaling systems./
作者:	Andrews, Burton W.
面頁冊數:	140 p.
附註:	Adviser: Pablo Iglesias.
Contained By:	Dissertation Abstracts International68-11B.
標題:	Biology, Cell. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3288425
ISBN:	9780549311607

Control and information-theoretic analysis of biological signaling systems.
Andrews, Burton W.

Control and information-theoretic analysis of biological signaling systems. - 140 p.

Adviser: Pablo Iglesias.

Thesis (Ph.D.)--The Johns Hopkins University, 2008.

Because the molecular processes that govern the behavior of many cellular organisms are highly stochastic, the proper functioning of these biological systems requires the ability to cope with biochemical noise. Using theoretical tools from engineering, this dissertation investigates the characteristics that enable cellular systems to cope with a variety of stochastic effects in the biochemical environment. We focus primarily on chemotaxis---the process by which cells migrate toward chemical attractants. Optimal filtering theory from control is used to show that the signal transduction network of E. coli effectively acts as a low-pass filter with a bandwidth that balances the need to detect noisy chemical cues accurately on a timescale set by the effects of diffusion. From information theory, we use rate distortion theory to show that gradient-sensing cells such as amoeba respond to spatial chemoattractant profiles using as little information as possible to achieve tolerable chemotactic performance levels. Our results suggest that many cellular organisms have evolved to respond to external chemicals in a manner that is consistent with optimal engineering designs. Moreover, the identification of key molecular components and processes that lead to this behavior provides biologists and experimentalists with further areas of study.

ISBN: 9780549311607Subjects--Topical Terms:

1017686
Biology, Cell.

Control and information-theoretic analysis of biological signaling systems.
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502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2008.
520 $a Because the molecular processes that govern the behavior of many cellular organisms are highly stochastic, the proper functioning of these biological systems requires the ability to cope with biochemical noise. Using theoretical tools from engineering, this dissertation investigates the characteristics that enable cellular systems to cope with a variety of stochastic effects in the biochemical environment. We focus primarily on chemotaxis---the process by which cells migrate toward chemical attractants. Optimal filtering theory from control is used to show that the signal transduction network of E. coli effectively acts as a low-pass filter with a bandwidth that balances the need to detect noisy chemical cues accurately on a timescale set by the effects of diffusion. From information theory, we use rate distortion theory to show that gradient-sensing cells such as amoeba respond to spatial chemoattractant profiles using as little information as possible to achieve tolerable chemotactic performance levels. Our results suggest that many cellular organisms have evolved to respond to external chemicals in a manner that is consistent with optimal engineering designs. Moreover, the identification of key molecular components and processes that lead to this behavior provides biologists and experimentalists with further areas of study.
520 $a This work highlights the fact that many theoretical tools from engineering can be used to study the biological mechanisms used by cells to survive and perform essential physiological functions. However, fundamental differences between biological and engineered systems can inspire theoretical contributions to engineering. We demonstrate this by concluding with a derivation of an extension to the internal model principle that is well-suited for models of biological systems.
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