Applications of algorithmic complexity include sequence searching and alignment, phylogenetic trees, structural classes in proteins, protein potentials, calcium and neural spike dynamics and DNA structure. Maximum entropy methods have also found widespread applications in spectral analysis and image reconstruction. Additional applications have appeared in nucleotide sequence analysis, protein dynamics, peptide structure and drug absorption. In addition to providing an underpinning for statistical methods, information theory also serves as a foundational framework for discussing fundamental biological processes. 50 years after Shannon's pioneering paper, entropy has become an integral part of several biological disciplines dealing with information storage and transmission.
Examples are kin recognition, evolution, or neuronal coding.
Information theory is particularly relevant in several areas of molecular biology. Genetic networks have also been investigated using information theoretical approaches. In all these cases, the information content of the system or phenomena is of intrinsic interest in its own right. The goal of this track is to highlight the breadth and diversity of applications of information theory to biological systems. This session will focus on the automated process of discovery of novel and useful patterns or motifs in molecular databases.