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Special Issue "Entropy in Genetics and Computational Biology"

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A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (31 March 2010)

Special Issue Editor

Guest Editor
Prof. Dr. Warren Ewens

324 Leidy Laboratories, Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
E-Mail
Fax: +1-215-898-8780

Special Issue Information

Dear Colleagues,

The concept of entropy arose in classical theoretical physics as describing a measure or randomness, or disorder, of a physical system. The second law of thermodynamics states that the entropy of a closed system increases with time: if a closed vessel initially contains hot air at one end and cold air at the other, then as time progresses the hot and cold air become increasingly mixed and this implies an increase in the entropy, or disorder, of the system. This is in effect a statistical law and in principle describes the most likely behaviour of the system. The huge number of atoms of air in the vessel implies however that this most likely behaviour is almost certain to arise, so that what is in principle a stochastic process can in practice be regarded as a deterministic one. In the biological world random events arise constantly, but here they are far more important than in the physical context just described. As just one example, the random transmission of genes from parent to offspring implies that the study of evolution as a genetic process must allow for this randomness. Thus this study involves quite complex mathematical stochastic processes, and developments in the theory of these processes have often been motivated by biological questions. Similarly advances in statistical theory have often, perhaps mainly, arisen in the biological and medical contexts. The analysis of medical data requires statistical methods to allow for the randomness inherent in the sampling process involved in obtaining these data. Thus entropy concepts, through statistics and stochastic process theory, pervade both medicine and biology.

Prof. Dr. Warren Ewens
Guest Editor

Keywords

  • randomness
  • uncertainty
  • sampling
  • genetics
  • evolution
  • accidents
  • stochastic processes
  • statistics

Published Papers (3 papers)

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Research

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Open AccessArticle Learning Genetic Population Structures Using Minimization of Stochastic Complexity
Entropy 2010, 12(5), 1102-1124; doi:10.3390/e12051102
Received: 21 February 2010 / Accepted: 28 April 2010 / Published: 5 May 2010
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Abstract
Considerable research efforts have been devoted to probabilistic modeling of genetic population structures within the past decade. In particular, a wide spectrum of Bayesian models have been proposed for unlinked molecular marker data from diploid organisms. Here we derive a theoretical framework for
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Considerable research efforts have been devoted to probabilistic modeling of genetic population structures within the past decade. In particular, a wide spectrum of Bayesian models have been proposed for unlinked molecular marker data from diploid organisms. Here we derive a theoretical framework for learning genetic population structure of a haploid organism from bi-allelic markers for which potential patterns of dependence are a priori unknown and to be explicitly incorporated in the model. Our framework is based on the principle of minimizing stochastic complexity of an unsupervised classification under tree augmented factorization of the predictive data distribution. We discuss a fast implementation of the learning framework using deterministic algorithms. Full article
(This article belongs to the Special Issue Entropy in Genetics and Computational Biology)
Open AccessArticle On the Interplay between Entropy and Robustness of Gene Regulatory Networks
Entropy 2010, 12(5), 1071-1101; doi:10.3390/e12051071
Received: 2 March 2010 / Revised: 10 April 2010 / Accepted: 28 April 2010 / Published: 4 May 2010
Cited by 17 | PDF Full-text (362 KB) | HTML Full-text | XML Full-text
Abstract
The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness
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The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness of a gene regulatory network, which can be measured as the ability to tolerate the random parameter fluctuation and to attenuate the effect of environmental noise, will be discussed from the robust H stabilization and filtering perspective. In this review, we will also discuss their balancing roles in evolution and potential applications in systems and synthetic biology. Full article
(This article belongs to the Special Issue Entropy in Genetics and Computational Biology)

Review

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Open AccessReview Entropy and Information Approaches to Genetic Diversity and its Expression: Genomic Geography
Entropy 2010, 12(7), 1765-1798; doi:10.3390/e12071765
Received: 1 April 2010 / Revised: 20 June 2010 / Accepted: 28 June 2010 / Published: 15 July 2010
Cited by 30 | PDF Full-text (294 KB) | HTML Full-text | XML Full-text
Abstract
This article highlights advantages of entropy-based genetic diversity measures, at levels from gene expression to landscapes. Shannon’s entropy-based diversity is the standard for ecological communities. The exponentials of Shannon’s and the related “mutual information” excel in their ability to express diversity intuitively, and
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This article highlights advantages of entropy-based genetic diversity measures, at levels from gene expression to landscapes. Shannon’s entropy-based diversity is the standard for ecological communities. The exponentials of Shannon’s and the related “mutual information” excel in their ability to express diversity intuitively, and provide a generalised method of considering microscopic behaviour to make macroscopic predictions, under given conditions. The hierarchical nature of entropy and information allows integrated modeling of diversity along one DNA sequence, and between different sequences within and among populations, species, etc. The aim is to identify the formal connections between genetic diversity and the flow of information to and from the environment. Full article
(This article belongs to the Special Issue Entropy in Genetics and Computational Biology)

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