Swarm intelligence algorithms in bioinformatics

Abstract

Research in bioinformatics necessitates the use of advanced computing tools for processing huge amounts of ambiguous and uncertain biological data. Swarm Intelligence (SI) has recently emerged as a family of nature inspired algorithms, especially known for their ability to produce low cost, fast and reasonably accurate solutions to complex search problems. In this chapter, we explore the role of SI algorithms in certain bioinformatics tasks like microarray data clustering, multiple sequence alignment, protein structure prediction and molecular docking. The chapter begins with an overview of the basic concepts of bioinformatics along with their biological basis. It also gives an introduction to swarm intelligence with special emphasis on two specific SI algorithms well-known as Particle Swarm Optimization (PSO) and Ant Colony Systems (ACS). It then provides a detailed survey of the state of the art research centered around the applications of SI algorithms in bioinformatics. The chapter concludes with a discussion on how SI algorithms can be used for solving a few open ended problems in bioinformatics. © 2008 Springer-Verlag Berlin Heidelberg.