One of Springer’s renowned Major Reference Works, this awesome achievement provides a comprehensive set of solutions to important algorithmic problems for students and researchers interested in quickly locating useful information. This first edition of the reference focuses on high-impact solutions from the most recent decade, while later editions will widen the scope of the work. All entries have been written by experts, while links to Internet sites that outline their research work are provided. The entries have all been peer-reviewed. This defining reference is published both in print and on line.

Under the Alfred P. Sloan Fellowship in Computational Biology, I have been afforded the opportunity to study phylogenetics--one of the most important and exciting disciplines in computational biology. A phylogeny depicts an evolutionary relationship among a set of organisms (or taxa). Typically, a phylogeny is represented by a binary tree, where modern organisms are placed at the leaves and ancestral organisms occupy internal nodes, with the edges of the tree denoting evolutionary relationships. The task of phylogenetics is to infer this tree from observations upon present-day organisms. Reconstructing phylogenies is a major component of modern research programs in many areas of biology and medicine, but it is enormously expensive. The most commonly used techniques attempt to solve NP-hard problems such as maximum likelihood and maximum parsimony, typically by bounded searches through an exponentially-sized tree-space. For example, there are over 13 billion possible trees for 13 organisms. Phylogenetic heuristics that quickly analyze large amounts of data accurately will revolutionize the biological field. This final report highlights my activities in phylogenetics during the two-year postdoctoral period at the University of New Mexico under Prof. Bernard Moret. Specifically, this report reports my scientific, community and professional activities as an Alfred P. Sloan Postdoctoral Fellow in Computational Biology.

Phylogenies, or evolutionary trees, are the basic structures necessary to think about and analyze differences between species. Statistical, computational, and algorithmic work in this field has been ongoing for four decades now, and there have been great advances in understanding. Yet no book has summarized this work. Inferring Phylogenies does just that in a single, compact volume. Phylogenies are inferred with various kinds of data. This book concentrates on some of the central ones: discretely coded characters, molecular sequences, gene frequencies, and quantitative traits. Also covered are restriction sites, RAPDs, and microsatellites.

"Phylogenetic inference involves the reconstruction of evolutionary relationships among species in the form of branching diagrams called trees. Specifically, certain biological structures common to all living organisms, such as morphological characteristics, protein sequences or DNA sequences can be compared. Differences and similarities in these characteristics among species are used to reconstruct the evolutionary relationships and draw trees. Many methods of tree reconstruction are currently used. The method of maximum parsimony for phylogenetic inference is a widely used algorithm which employs the hypothesis that the most likely tree for a given group of data will be the one which uses the least number of changes from an origin (root of the tree) to the terminal taxa. The problems and corresponding solution algorithms associated with these searches are frequently implemented on single-processor systems, and can take weeks to complete for large data sets. Parallelization of these algorithms is therefore an important area of development in the bioinformatics community. A free license, open-source, parallel implementation of a phylogenetic inference program using maximum parsimony has yet to be developed, and it is the aim of this thesis to provide such a tool. It is hoped that the tool will work transparently with one of the most popular suites of free phylogenetic inference tools called PHYLIP, developed by Joe Felsenstein at the University of Washington, by accepting and generating the same format of input and output data. The tool would be a first step towards providing the academic community and others with improvements in performance and capabilities (through parallelization) over the currently available free distributions of phylogenetic inference programs using parsimony, allowing for larger volumes of data to be analyzed in a reduced amount of time"--Abstract.

The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology of