Literature DB >> 10908337

Evolution of biological information.

T D Schneider1.   

Abstract

How do genetic systems gain information by evolutionary processes? Answering this question precisely requires a robust, quantitative measure of information. Fortunately, 50 years ago Claude Shannon defined information as a decrease in the uncertainty of a receiver. For molecular systems, uncertainty is closely related to entropy and hence has clear connections to the Second Law of Thermodynamics. These aspects of information theory have allowed the development of a straightforward and practical method of measuring information in genetic control systems. Here this method is used to observe information gain in the binding sites for an artificial 'protein' in a computer simulation of evolution. The simulation begins with zero information and, as in naturally occurring genetic systems, the information measured in the fully evolved binding sites is close to that needed to locate the sites in the genome. The transition is rapid, demonstrating that information gain can occur by punctuated equilibrium.

Mesh:

Year:  2000        PMID: 10908337      PMCID: PMC102656          DOI: 10.1093/nar/28.14.2794

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  24 in total

1.  Genome complexity, robustness and genetic interactions in digital organisms.

Authors:  R E Lenski; C Ofria; T C Collier; C Adami
Journal:  Nature       Date:  1999-08-12       Impact factor: 49.962

2.  Rapid evolution of a geographic cline in size in an introduced fly.

Authors:  R B Huey; G W Gilchrist; M L Carlson; D Berrigan; L Serra
Journal:  Science       Date:  2000-01-14       Impact factor: 47.728

3.  The middle way.

Authors:  R B Laughlin; D Pines; J Schmalian; B P Stojkovic; P Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  2000-01-04       Impact factor: 11.205

4.  Measuring molecular information.

Authors:  T D Schneider
Journal:  J Theor Biol       Date:  1999-11-07       Impact factor: 2.691

5.  Theory of molecular machines. I. Channel capacity of molecular machines.

Authors:  T D Schneider
Journal:  J Theor Biol       Date:  1991-01-07       Impact factor: 2.691

6.  High information conservation implies that at least three proteins bind independently to F plasmid incD repeats.

Authors:  N D Herman; T D Schneider
Journal:  J Bacteriol       Date:  1992-06       Impact factor: 3.490

7.  Suggestions as to quantitative measurement of rates of evolution.

Authors:  J B S HALDANE
Journal:  Evolution       Date:  1949-03       Impact factor: 3.694

8.  Theory of molecular machines. II. Energy dissipation from molecular machines.

Authors:  T D Schneider
Journal:  J Theor Biol       Date:  1991-01-07       Impact factor: 2.691

9.  OxyR and SoxRS regulation of fur.

Authors:  M Zheng; B Doan; T D Schneider; G Storz
Journal:  J Bacteriol       Date:  1999-08       Impact factor: 3.490

10.  Features of spliceosome evolution and function inferred from an analysis of the information at human splice sites.

Authors:  R M Stephens; T D Schneider
Journal:  J Mol Biol       Date:  1992-12-20       Impact factor: 5.469

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  38 in total

1.  Strong minor groove base conservation in sequence logos implies DNA distortion or base flipping during replication and transcription initiation.

Authors:  T D Schneider
Journal:  Nucleic Acids Res       Date:  2001-12-01       Impact factor: 16.971

Review 2.  Consensus sequence Zen.

Authors:  Thomas D Schneider
Journal:  Appl Bioinformatics       Date:  2002

3.  An information theoretic approach to macromolecular modeling: II. Force fields.

Authors:  Tiba Aynechi; Irwin D Kuntz
Journal:  Biophys J       Date:  2005-11       Impact factor: 4.033

4.  Twenty Years of Delila and Molecular Information Theory: The Altenberg-Austin Workshop in Theoretical Biology Biological Information, Beyond Metaphor: Causality, Explanation, and Unification Altenberg, Austria, 11-14 July 2002.

Authors:  Thomas D Schneider
Journal:  Biol Theory       Date:  2006

5.  Very small mobile repeated elements in cyanobacterial genomes.

Authors:  Jeff Elhai; Michiko Kato; Sarah Cousins; Peter Lindblad; José Luis Costa
Journal:  Genome Res       Date:  2008-07-03       Impact factor: 9.043

Review 6.  Mechanisms and evolution of control logic in prokaryotic transcriptional regulation.

Authors:  Sacha A F T van Hijum; Marnix H Medema; Oscar P Kuipers
Journal:  Microbiol Mol Biol Rev       Date:  2009-09       Impact factor: 11.056

Review 7.  Biomolecular information gained through in vitro evolution.

Authors:  Takuyo Aita; Yuzuru Husimi
Journal:  Biophys Rev       Date:  2009-12-15

8.  Statistical tests for natural selection on regulatory regions based on the strength of transcription factor binding sites.

Authors:  Alan M Moses
Journal:  BMC Evol Biol       Date:  2009-12-09       Impact factor: 3.260

9.  The Jackprot Simulation Couples Mutation Rate with Natural Selection to Illustrate How Protein Evolution Is Not Random.

Authors:  Guillermo Paz-Y-Miño C; Avelina Espinosa; Chunyan Y Bai
Journal:  Evolution (N Y)       Date:  2011-03-24

10.  Inferring selection on amino acid preference in protein domains.

Authors:  Alan M Moses; Richard Durbin
Journal:  Mol Biol Evol       Date:  2008-12-18       Impact factor: 16.240

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