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Special Issue "Emergence of Information in Evolutionary Processes"

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

Deadline for manuscript submissions: closed (28 December 2011)

Special Issue Editor

Guest Editor
Prof. Dr. Kay Hamacher

AG Bioinformatics and theo. Biology Technische Universität Darmstadt, Schnittspahnstr. 10 64287 Darmstadt, Germany
Interests: computational biology; statistical biophysics; simulation and scientific computing; coarse-graining and multi-scale modeling; optimization

Special Issue Information

Dear Colleagues,

Biology has become a quantitative science and is seen more frequently as an information science with close links to computer science - culminating in the raise of bioinformatics and computational biology as well-established fields within biology and computer science itself.

The underlying dynamics of all biological process is evolution. Although mathematical models of evolution were introduced early on, the widespread availability of sequence and structural data opens new horizons for model derivation, method development and empirical insight in particular. New biology could nowadays be conceived by combining models and available data.

Now, evolutionary processes are inherently stochastic in nature, but at the same time they store, propagate, adapt, and combine information from aeons of selective pressure of competitors and the ecological environment. Information theoretical concepts and measures are therefore of fundamental importance to help evolutionary biology becoming quantitative.

This Special Issue of "Entropy" is devoted to this endeavor. We would like to publish novel research on:

  • method development to quantify evolutionary processes
  • information theoretical concepts to define evolutionary processes in a holistic fashion
  • applications to open challenges in biology
  • entropy concepts to understand adaption in natural and artificial systems
  • entropy measures for drug resistance
  • and novel ideas on the combination of information theory and evolution.

Kay Hamacher
Guest Editor

Keywords

  • evolutionary dynamics
  • quasi-species
  • phylogeny
  • information
  • stochastic processes
  • biological and ecological networks
  • network dynamics
  • sequence entropy
  • mutual information
  • evolutionary operators
  • fixation
  • co-evultion
  • host-parasite interaction
  • viral-host evolution

Published Papers (2 papers)

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Research

Open AccessArticle Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved towards Low-Entropy Dynamics
Entropy 2012, 14(6), 993-1010; doi:10.3390/e14060993
Received: 30 April 2012 / Revised: 31 May 2012 / Accepted: 31 May 2012 / Published: 5 June 2012
Cited by 2 | PDF Full-text (3051 KB) | HTML Full-text | XML Full-text
Abstract
Cellular automata (CA) are a remarkably efficient tool for exploring general properties of complex systems and spatiotemporal patterns arising from local rules. Totalistic cellular automata, where the update rules depend only on the density of neighboring states, are at the same time a versatile [...] Read more.
Cellular automata (CA) are a remarkably efficient tool for exploring general properties of complex systems and spatiotemporal patterns arising from local rules. Totalistic cellular automata, where the update rules depend only on the density of neighboring states, are at the same time a versatile tool for exploring dynamical processes on graphs. Here we briefly review our previous results on cellular automata on graphs, emphasizing some systematic relationships between network architecture and dynamics identified in this way. We then extend the investigation towards graphs obtained in a simulated-evolution procedure, starting from Erdő s–Rényi (ER) graphs and selecting for low entropies of the CA dynamics. Our key result is a strong association of low Shannon entropies with a broadening of the graph’s degree distribution. Full article
(This article belongs to the Special Issue Emergence of Information in Evolutionary Processes)
Open AccessArticle Towards an Evolutionary Model of Animal-Associated Microbiomes
Entropy 2011, 13(3), 570-594; doi:10.3390/e13030570
Received: 16 December 2010 / Revised: 19 February 2011 / Accepted: 22 February 2011 / Published: 25 February 2011
Cited by 17 | PDF Full-text (931 KB) | HTML Full-text | XML Full-text
Abstract
Second-generation sequencing technologies have granted us greater access to the diversity and genetics of microbial communities that naturally reside endo- and ecto-symbiotically with animal hosts. Substantial research has emerged describing the diversity and broader trends that exist within and between host species [...] Read more.
Second-generation sequencing technologies have granted us greater access to the diversity and genetics of microbial communities that naturally reside endo- and ecto-symbiotically with animal hosts. Substantial research has emerged describing the diversity and broader trends that exist within and between host species and their associated microbial ecosystems, yet the application of these data to our evolutionary understanding of microbiomes appears fragmented. For the most part biological perspectives are based on limited observations of oversimplified communities, while mathematical and/or computational modeling of these concepts often lack biological precedence. In recognition of this disconnect, both fields have attempted to incorporate ecological theories, although their applicability is currently a subject of debate because most ecological theories were developed based on observations of macro-organisms and their ecosystems. For the purposes of this review, we attempt to transcend the biological, ecological and computational realms, drawing on extensive literature, to forge a useful framework that can, at a minimum be built upon, but ideally will shape the hypotheses of each field as they move forward. In evaluating the top-down selection pressures that are exerted on a microbiome we find cause to warrant reconsideration of the much-maligned theory of multi-level selection and reason that complexity must be underscored by modularity. Full article
(This article belongs to the Special Issue Emergence of Information in Evolutionary Processes)
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