Comparing dynamical features of biological objects/populations
with respect to targeting and categorizing their particular dynamical 
states. Identification of typical or most significant properties 
(ioinic/molecular markers) of an evolving process of interest. 
Possible extension towards short-term predictions of the system's dynamical behavior.

Advanced computational and statistical techniques will be applied or 
adapted for specific applications. In particular, algorithms developed for 
large data sets analyses as such used in different branches of 
bioinformatics or epidemiology will be studied and analyzed. The goal 
is to improve their performance with respect to sufficient sensitivity 
under dimensionality reduction, signal-to-noise ratio, and periodic 
patterns identification. At first, neighbor-joining methods and forward 
floating search will be targeted.