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7 - From Entropy to Information: Biased Typewriters and the Origin of Life
- from Part II - Bio from Bit
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- By Christoph Adami, Michigan State University, Thomas Labar, Michigan State University
- Edited by Sara Imari Walker, Arizona State University, Paul C. W. Davies, Arizona State University, George F. R. Ellis, University of Cape Town
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- Book:
- From Matter to Life
- Published online:
- 02 March 2017
- Print publication:
- 23 February 2017, pp 130-154
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Summary
So much has been written about the possible origins of life on Earth (see, e.g., the popular books by Deamer, 1994; deDuve, 1995; Koonin, 2011; Morowitz, 2004) that it sometimes seems that – barring an extraordinary breakthrough in experimental biochemistry (e.g., Patel et al., 2015), or the discovery of the remnants of an ancient biochemistry (Davies et al., 2009) – nothing new can be said about the problem. But such a point of view does not take into account that perhaps not all the tools of scientific inquiry have been fully utilized in this endeavor to unravel our ultimate origin on this planet. Indeed, originof- life research has historically been confined to a fairly narrow range of disciplines, such as biochemistry and geochemistry. Today, amuch broader set of tools is being unleashed on this problem, including mathematical (England, 2013; Smith, 2008; Vetsigian et al., 2006) and computational approaches (Mathis et al., 2015; Nowak and Ohtsuki, 2008; Segre et al., 2000; Vasas et al., 2012; Walker et al., 2012). Computational approaches to the study of possible origins of life are often derided because they lack a particular feature of biochemistry, or “because they do not take into account the specific properties of individual organic compounds and polymers” (Lazcano and Miller, 1996). Such a point of view ignores the possibility that life may be not a feature that is dependent on a particular biochemistry (Benner et al., 2004), but could instead be a feature of any chemistry that is capable of encoding information.
If the one invariant in life is information (information about how to replicate, that is), it then becomes imperative to understand the general principles by which information could arise by chance. It is generally understood that evolution, viewed as a computational process (Adami, 1998; Mayfield, 2013), leads to an increase in information on average. The amount of information that evolution has accumulated to date differs from organism to organism, of course, and precise numbers are not known. A rough estimate of the amount of information stored in an organism's genome can be obtained by calculating the amount of functional DNA in an organism.
List of Contributors
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- By Adam K. Anderson, Jorge Armony, Anthony P. Atkinson, Sonia Bishop, Carolin Brück, Roberto Cabeza, Frances S. Chen, Hugo D. Critchley, Mauricio R. Delgado, Ricardo de Oliveira-Souza, Gregor Domes, Judith Domínguez-Borràs, Joseph E. Dunsmoor, Thomas Ethofer, Dominic S. Fareri, Lesley K. Fellows, Sophie Forster, Katherine Gardhouse, Nathalie George, Jay A. Gottfried, Jung Eun Han, Ahmad R. Hariri, Neil A. Harrison, Markus Heinrichs, Alisha C. Holland, Andreas Keil, Elizabeth A. Kensinger, Johanna Kissler, Olga Klimecki, Stefan Koelsch, Sylvia D. Kreibig, Benjamin Kreifelts, Robert Kumsta, Kevin S. LaBar, Eamon J. McCrory, Aprajita Mohanty, Jorge Moll, John P. O’Doherty, Leticia Oliveira, Mirtes Pereira, Luiz Pessoa, K. Luan Phan, Pierre Rainville, David Sander, Annett Schirmer, Catherine L. Sebastian, Tania Singer, Chandra Sekhar Sripada, Peggy L. St. Jacques, Essi Viding, Patrik Vuilleumier, Dirk Wildgruber, Amy Winecoff, Roland Zahn
- Edited by Jorge Armony, McGill University, Montréal, Patrik Vuilleumier, Université de Genève
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- Book:
- The Cambridge Handbook of Human Affective Neuroscience
- Published online:
- 05 February 2013
- Print publication:
- 21 January 2013, pp xi-xii
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