Literature DB >> 10882561

Molecular evolution of catalysis.

C V Forst1.   

Abstract

In this paper, we consider the evolutionary dynamics of catalytically active species with a distinct genotype-phenotype relationship. Folding landscapes of RNA molecules serve as a paradigm for this relationship with essential neutral properties. The landscape itself is partitioned by phenotypes (realized as RNA secondary structures). To each genotype (represented as a sequence) a structure is assigned in a unique way. The set of all sequences which map into a particular structure is modeled as a random graph in sequence space (the so-called neutral network). A catalytic network is realized as a random digraph with maximal out-degree two and secondary structures as vertex sets. A population of catalytic RNA molecules shows significantly different behavior compared to a deterministic description: hypercycles are able to co-exist and out-compete a parasite with superior catalytic support. A "switching" between different dynamic organizations of the network can be observed, dynamical stability of hypercyclic organizations against errors and the existence of an error-threshold of catalysis can be reported. Copyright 2000 Academic Press.

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Year:  2000        PMID: 10882561     DOI: 10.1006/jtbi.2000.2076

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  3 in total

1.  Comprehensive experimental fitness landscape and evolutionary network for small RNA.

Authors:  José I Jiménez; Ramon Xulvi-Brunet; Gregory W Campbell; Rebecca Turk-MacLeod; Irene A Chen
Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-26       Impact factor: 11.205

2.  Evolution of complexity in RNA-like replicator systems.

Authors:  Nobuto Takeuchi; Paulien Hogeweg
Journal:  Biol Direct       Date:  2008-03-27       Impact factor: 4.540

3.  Neutral network sizes of biological RNA molecules can be computed and are not atypically small.

Authors:  Thomas Jörg; Olivier C Martin; Andreas Wagner
Journal:  BMC Bioinformatics       Date:  2008-10-30       Impact factor: 3.169

  3 in total

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