Computation and Biology A Joint Venture

and traveling salesman. A little background in molecular As we swim in a sea of data—both genomic and microarbiology would also be helpful. There is a brief chapter ray—we need good computational tools to understand titled “All You Need to Know about Molecular Biology,” the biological significance of the information we generbut its first sentence is “Well, not really, of course, see ate. One such tool that has emerged from the field of Lewin, 1999 [Genes VII], for an introduction” (p. 271). computational molecular biology and is used widely by Each chapter in Computational Molecular Biology bebiologists is the sequence comparison tool BLAST. gins with an introduction to the computational and bioOther tools include multiple sequence alignment softlogical ideas without any formulas. For example, to introware such as ClustalX and contig assembly software duce the computational problems associated with such as Phrap. With the draft of the human genome physical mapping, Pevzner describes the experiments sequence available now and the mouse genome seused in the physical mapping of cystic fibrosis. To motiquence available shortly, we must increasingly turn to vate the problem, he uses an analogy of having “... sevthe field of computational molecular biology to build eral copies of a book cut by scissors into thousands of additional tools to help us make sense of these data. pieces. Each copy is cut in an individual way such that a As an undergraduate mathematics major at the Unipiece from one copy many overlap a piece from another versity of Wisconsin in the 1970s, I was totally enthralled copy.....” (p. 5). This is characteristic of the style of the with population and quantitative genetics after taking book. For each computational problem, he gives an an introductory genetics course from Professor James analogy that requires no biological knowledge and then F. Crow. At the time, this area presented a way to apply describes the biological problem for which a computamy mathematical interest in the exciting field of genetics. tional solution is required. This is a good style and makes Now, the potential areas of research for mathematicians the introductory section of each chapter accessible to and computer scientists interested in the field of molecbiologists interested in learning about some of the comular biology are equally rich and much more diverse. putational challenges in the field. The question does arise, however, how best to generPevzner covers problems drawn primarily from genoate interest and train the next generation of computamics and sequence analysis. Included are chapters on tional biologists? There is a strong need to entice comComputational Gene Hunting, Restriction Mapping, Map puter scientists into the field of computational biology Assembly, Sequencing, Sequence Comparison, DNA to solve such problems as, for example, promoter recogArrays, Multiple Alignment, Finding Signals in DNA, nition in genomic sequence, analytical tools for underGene Prediction, Genome Rearrangement, and Compustanding data from microarray experiments, and accutational Proteomics. Readers interested in structural rate prediction of protein folds from sequences. Many biology-related topics including such topics as preuniversities and colleges are requiring all undergradudicting structure from sequence or other topics not covates to take an introductory course in biology. If you ered by Pevzner will need to turn to other books. See teach such a course, Computational Molecular Biology for example Bioinformatics: The Machine Learning Apby Pevzner provides a useful high-level introduction to proach (Adaptive Computation and Machine Learning) selected computational problems and solutions in moby Pierre Baldi and Soren Brunak, 1998; Algorithms on lecular biology, which could be useful for those trained in Strings, Trees, and Sequences: Computer Science and computer science or mathematics who want to become Computational Biology by Dan Gusfield, 1997; or Comfamiliar with the problems that interest biologists. putational Methods in Molecular Biology edited by Ste-