Special Issue "Emergence of Information in Evolutionary Processes"

Guest Editor
Prof. Dr. Kay Hamacher
AG Bioinformatics and theo. Biology Technische Universität Darmstadt, Schnittspahnstr. 10 64287 Darmstadt, Germany
E-Mail: hamacher@bio.tu-darmstadt.de
Interests: computational biology; statistical biophysics; simulation and scientific computing; coarse-graining and multi-scale modeling; optimization

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

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. 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 1200 CHF (Swiss Francs).

• 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

Title: Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved Towards Low-Entropy Dynamics
Authors: Carsten Marr and Marc-Thorsten Hütt
Affiliation: E-Mails: m.huett@jacobs-university.de; carstenmarr@googlemail.com
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 states, are at the same time a versatile tool for also 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 Erdös-Rényi (ER) graphs and selecting for low entropies of the CA dynamics. Our key result is a strong systematic association of low Shannon entropies and a broadening of the graph's degree distribution.