Bioinformatics and Management Science: Some Common Tools and Techniques

Ali E. Abbas () and Susan P. Holmes ()
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Ali E. Abbas: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Susan P. Holmes: Department of Statistics, Stanford University, Stanford, California 94305

Operations Research, 2004, vol. 52, issue 2, 165-190

Abstract: In April of 2003, Science (2003) and Nature (2003) published special issues marking two significant achievements in the history of science: the 50th anniversary of discovering the double helical structure of the DNA, and the completion of the Human Genome Project. The first discovery led to a new age in genetics, and the second event marked the beginning of a new era that uses the genome in medicine. The international efforts to determine the human DNA sequence and assess its ethical, legal, and social implications started in 1990. Since then, the data from the project has been available in public databases for researchers and scientists around the world. The vast increase in biological data led to increasing interest in computational biology and an emerging multidisciplinary research area known as bioinformatics. Most people working in this area have mathematics, biology, biochemistry, or computer science backgrounds and have learned about the field by using tools from another discipline to answer questions in biology. The current challenge is to utilize the genome data to its full extent and to develop tools that improve our understanding of biological pathways and accelerate drug discovery. Many of the algorithms needed to solve these problems have management science and operations research aspects. This paper introduces some of the fundamental problems in bioinformatics to an operations research audience and demonstrates the application of management science tools in their formulation and solution.

Keywords: analysis of algorithms: computational complexity; dynamic programming: Markov; health care; diagnosis: pharmaceutical; probability; stochastic model applications (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (3)

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