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Information 2012, 3(4), 739-750; doi:10.3390/info3040739
Article

Information Theory and Computational Thermodynamics: Lessons for Biology from Physics

Received: 7 November 2012; in revised form: 17 November 2012 / Accepted: 19 November 2012 / Published: 22 November 2012
(This article belongs to the Special Issue Information and Energy/Matter)
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Abstract: We survey a few aspects of the thermodynamics of computation, connecting information, thermodynamics, computability and physics. We suggest some lines of research into how information theory and computational thermodynamics can help us arrive at a better understanding of biological processes. We argue that while a similar connection between information theory and evolutionary biology seems to be growing stronger and stronger, biologists tend to use information simply as a metaphor. While biologists have for the most part been influenced and inspired by information theory as developed by Claude Shannon, we think the introduction of algorithmic complexity into biology will turn out to be a much deeper and more fruitful cross-pollination.
Keywords: thermodynamics of computation; algorithmic probability; information theory; computability and Turing universality thermodynamics of computation; algorithmic probability; information theory; computability and Turing universality
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zenil, H. Information Theory and Computational Thermodynamics: Lessons for Biology from Physics. Information 2012, 3, 739-750.

AMA Style

Zenil H. Information Theory and Computational Thermodynamics: Lessons for Biology from Physics. Information. 2012; 3(4):739-750.

Chicago/Turabian Style

Zenil, Hector. 2012. "Information Theory and Computational Thermodynamics: Lessons for Biology from Physics." Information 3, no. 4: 739-750.


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