Preface -- Complete and Exact Peptide Sequence Analysis based on Propositional Logic -- Divide & Conquer Strategies for Protein Structure Prediction -- Secondary Structure Classification of Isoform Protein Markers in Oncology -- Protein Fold Recognition by using Markov Logic Networks -- Mining Spatial Association Rules for Composite Motif Discovery -- Modeling Biochemical Pathways -- Haplotype Inference using Propositional Satisfiability -- Estimating Phylogenies from Molecular Data -- Population Stratification Analysis in Genome-Wide Association Studies -- Predicting and Measuring the Sequence Distribution of Addition Polymers -- Predicting and Measuring the Sequence Distribution of Condensation Polymers -- Index.

Many problems arising in biological, chemical and medical research, that could not be solved in the past due to their dimension and complexity, are nowadays tackled by means of automatic elaboration, thus creating the emerging field of Bioinformatics. However, the success of such approaches depends not only on brute computational strength of the computers on which the solution procedures run, but also, and often critically, on the mathematical quality of the models and of the algorithms underlying those solution procedures. The present volume offers a detailed overview of some of the most interesting mathematical approaches to sequence analysis and other sequence related problems. Special emphasis is devoted to problems concerning the most relevant biopolymers (proteins and genetic sequences), but the exposition is not limited to them. The target audience consists of researchers from many areas of Bioinformatics interested in sequence analysis problems either from a theoretical and mathematical point of view, such as mathematicians and computer scientists, or for more applicative and production-oriented reasons, such as biologists and medical researchers or practitioners working for chemical or pharmaceutical companies. The book should moreover be of use to mathematics students learning computational biology, or to biology students learning bioinformatics.