Special Issue "Emergence of Information in Evolutionary Processes"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (28 December 2011)
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
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.
- evolutionary dynamics
- stochastic processes
- biological and ecological networks
- network dynamics
- sequence entropy
- mutual information
- evolutionary operators
- host-parasite interaction
- viral-host evolution