Buch, Englisch, 442 Seiten, Format (B × H): 164 mm x 242 mm, Gewicht: 757 g
An Algorithmic Approach
Buch, Englisch, 442 Seiten, Format (B × H): 164 mm x 242 mm, Gewicht: 757 g
Reihe: Chapman & Hall/CRC Computational Biology Series
ISBN: 978-1-58488-683-9
Verlag: Taylor & Francis Inc
The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics
Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure.
Helps Students Go Further in Their Study of Structural Biology
Following some introductory material in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design.
At the Crossroads of Biology, Mathematics, and Computer Science
Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).
Zielgruppe
Undergraduate
Autoren/Hrsg.
Weitere Infos & Material
Preface. The Study of Structural Bioinformatics. Introduction to Macromolecular Structure. Data Sources, Formats, and Applications. Dynamic Programming. RNA Secondary Structure Prediction. Protein Sequence Alignment. Protein Geometry. Coordinate Transformations. Structure Comparison, Alignment, and Superposition. Machine Learning. Overview of the Appendices. Index.
