Nouhad J Rizk

Many of the today's problems, such as those involved in weather prediction, aerodynamics, and genetic mapping, require tremendous computational resources to be solved accurately. These applications are computationally very intensive and require vast amounts of processing power and memory requirements. Therefore, to give accurate results, powerful computers are needed to reduce the run time, for example, finding genes in DNA sequences, predicting the structure and functions of new proteins, clustering proteins into families, aligning similar proteins, and generating phylogenetic trees to examine evolutionary relationships all need complex computations. To develop parallel computing programs for such kinds of computational biology problems, the role of a computer architect is important; his or her role is to design and engineer the various levels of a computer system to maximize performance and programmability within limits of technology and cost. Thus, parallel computing is an effective way to tackle problems in biology; multiple processors being used to solve the same problem. The scaling of memory with processors enables the solution of larger problems than would be otherwise possible, while modeling a solution is as much important as the computation.

In this chapter, after an introduction to genes and genomes, we describe some efficient parallel algorithms that efficiently solve applications in computational biology. An evolutionary approach to computational biology is presented based first on the search space, which is the set of all possible solutions. The second factor used for the formulation of an optimization problem is the determination of a fitness function that measures how good a particular answer is. Finally, a significant deviation from the standard parallel solution to genetic parallel algorithms approach theory is pointed out by arguing that parallel computational biology is an important sub-discipline that merits significant research attention and that combining different solution paradigms is worth implementing.

Parallel Computing for Bioinformatics and Computational Biology, Edited by Albert Y. Zomaya Copyright © 2006 John Wiley & Sons, Inc.

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