A model-independent approach to infer hierarchical codon substitution dynamics

<p>Abstract</p> <p>Background</p> <p>Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on c...

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Main Authors: Jacobi Martin, Görnerup Olof
Format: Article
Language:English
Published: BMC 2010-04-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/11/201
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spelling doaj-3b4574efb4af4ab2a1d9cadeb89e57322020-11-24T23:51:49ZengBMCBMC Bioinformatics1471-21052010-04-0111120110.1186/1471-2105-11-201A model-independent approach to infer hierarchical codon substitution dynamicsJacobi MartinGörnerup Olof<p>Abstract</p> <p>Background</p> <p>Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems.</p> <p>Results</p> <p>We have applied the techniques on an empirically derived Markov model of the codon substitution process that is provided in the literature. Without system specific knowledge of the substitution process, the techniques manage to "blindly" identify multiple levels of dynamics; from amino acid substitutions (via the standard genetic code) to higher order dynamics on the level of amino acid groups. We hypothesize that the acquired groups reflect earlier versions of the genetic code.</p> <p>Conclusions</p> <p>The results demonstrate the applicability of the techniques. Due to their generality, we believe that they can be used to coarse grain and identify hierarchical organization in a broad range of other biological systems and processes, such as protein interaction networks, genetic regulatory networks and food webs.</p> http://www.biomedcentral.com/1471-2105/11/201
collection DOAJ
language English
format Article
sources DOAJ
author Jacobi Martin
Görnerup Olof
spellingShingle Jacobi Martin
Görnerup Olof
A model-independent approach to infer hierarchical codon substitution dynamics
BMC Bioinformatics
author_facet Jacobi Martin
Görnerup Olof
author_sort Jacobi Martin
title A model-independent approach to infer hierarchical codon substitution dynamics
title_short A model-independent approach to infer hierarchical codon substitution dynamics
title_full A model-independent approach to infer hierarchical codon substitution dynamics
title_fullStr A model-independent approach to infer hierarchical codon substitution dynamics
title_full_unstemmed A model-independent approach to infer hierarchical codon substitution dynamics
title_sort model-independent approach to infer hierarchical codon substitution dynamics
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2010-04-01
description <p>Abstract</p> <p>Background</p> <p>Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems.</p> <p>Results</p> <p>We have applied the techniques on an empirically derived Markov model of the codon substitution process that is provided in the literature. Without system specific knowledge of the substitution process, the techniques manage to "blindly" identify multiple levels of dynamics; from amino acid substitutions (via the standard genetic code) to higher order dynamics on the level of amino acid groups. We hypothesize that the acquired groups reflect earlier versions of the genetic code.</p> <p>Conclusions</p> <p>The results demonstrate the applicability of the techniques. Due to their generality, we believe that they can be used to coarse grain and identify hierarchical organization in a broad range of other biological systems and processes, such as protein interaction networks, genetic regulatory networks and food webs.</p>
url http://www.biomedcentral.com/1471-2105/11/201
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