http://www.mdpi.com/journal/entropy/special_issues/entropy_genetics/ 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 Submission All manuscripts should be submitted to entropy@mdpi.com with a copy to the Guest Editor. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI. Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this Open Access journal is 1000 CHF per accepted paper. http://www.mdpi.com/1099-4300/12/7/1765/ 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. http://www.mdpi.com/1099-4300/12/7/1765/ Thu, 15 Jul 2010 00:00:00 CEST Entropy 2010-07-15 12 7 Review 1765 1798 1099-4300 Entropy and Information Approaches to Genetic Diversity and its Expression: Genomic Geography 2010-07-15 doi: 10.3390/e12071765 Sherwin http://www.mdpi.com/1099-4300/12/5/1102/ 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. http://www.mdpi.com/1099-4300/12/5/1102/ Wed, 05 May 2010 00:00:00 CEST Entropy 2010-05-05 12 5 Article 1102 1124 1099-4300 Learning Genetic Population Structures Using Minimization of Stochastic Complexity 2010-05-05 doi: 10.3390/e12051102 Corander Gyllenberg Koski http://www.mdpi.com/1099-4300/12/5/1071/ 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. http://www.mdpi.com/1099-4300/12/5/1071/ Tue, 04 May 2010 00:00:00 CEST Entropy 2010-05-04 12 5 Article 1071 1101 1099-4300 On the Interplay between Entropy and Robustness of Gene Regulatory Networks 2010-05-04 doi: 10.3390/e12051071 Chen Li