What is this book good for? Imagine you are a computer scientist working in the bioinformatics area. P- bably you will be a member of a highly interdisciplinary team consisting of biologists, chemists, mathematicians, computer scientists ranging from p- grammers to algorithm engineers, and eventually people from various further ?elds. A major problem within such interdisciplinary teams is always to ?nd some common language, and, for each member of some discipline, to have profound knowledge of what are the notions, basic concepts and goals of the other participating disciplines, as well as of what they can contribute to the solutionofonesownproblems. Thisdoes,ofcourse,notmeanthatacomputer scientist should do the job of the biologist. Nevertheless, a computer scientist should be able to understand what a biologist deals with. On the other hand, the biologist should not do the computer scientists job, but should know what computer science and algorithm engineering might contribute to thesolution of her/his problems, and also how problems should be stated in order for the computer scientist to understand them. This book primarily aims to show the potential that algorithm engin- ring o?ers for the solution of core bioinformatics problems.
There are fundamental principles for problem analysis and algorithm design that are continuously used in bioinformatics. This book concentrates on a clear presentation of these principles, presenting them in a self-contained, mathematically clear and precise manner, and illustrating them with lots of case studies from main fields of bioinformatics (e.g. sequencing and mapping, string storage and manipulation, pattern matching, alignment, gene identification, genome rearrangement, structure prediction, regulatory networks, pseudoknot detection). Emphasis is laid on algorithmic "pearls" of bioinformatics, showing that things may get rather simple when taking a proper view into them. The book closes with a thorough bibliography, ranging from classic research results to very recent findings, providing many pointers for future research. Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background.
This book highlights basic paradigms of problem analysis and algorithm design in the context of core bioinformatics problems. Mathematically demanding themes are put across to the reader by properly chosen representations with the aid of lots of illustrations.
(Prof. Dr. Amitava Datta, UWA, Perth, Australia)
Written by a theoretical computer scientist, the book perfectly complements standard textbooks from the point of view of problem solving methods continuously applied in bioinformatics.
(Prof. Dr. Georg Fuellen, Univ. Greifswald, Germany)
