Learning the LingoA Cell Biologist's Guide to Modeling and Bioinformatics, by HolmesRaquell M.; 2007; 203 pp.; John Wiley and Sons (Hoboken, NJ); ISBN-10:0471164208

Laurie J. Heyer
2008 CBE - Life Sciences Education  
One of my greatest challenges over the last six years of teaching an undergraduate course in bioinformatics has been finding an appropriate textbook. Although the field of bioinformatics has experienced explosive growth over the last decade, the concomitant increase in available books in this field has failed to produce the right textbook for my course and my students. Each book targets a different audience and applies a different approach to the subject, but the combinatorial diversity results
more » ... in near misses. To date, bioinformatics books have been aimed at mathematicians, statisticians, computer scientists, molecular biologists, or pharmaceutical and medical researchers. They are pitched at the beginning, middle, or advanced undergraduate level, or to technicians, graduate students, or practicing scientists. Authors take theoretical, abstract, practical, hands-on, and case-based approaches. Topics are focused or comprehensive according to the authors' own experiences and individual opinion of what constitutes bioinformatics. Each book, with its unique combination of target audience, level of difficulty, pedagogical approach and coverage, fills a small niche of the broad computational biology landscape. In A Cell Biologist's Guide to Modeling and Bioinformatics, Holmes attempts the admirable but nearly impossible task of introducing not only bioinformatics, but also computational cell biology, in a slim volume aimed at both practicing biologists and undergraduate students. The result is a whirlwind tour of mathematical and computational approaches to biology. The reader is exposed to an incredible range of ideas, some in sufficient depth to be put directly into practice, others just skimming the surface of available databases and tools. After outlining the purpose and value of computational and mathematical approaches to biology in a six-page introductory chapter, Holmes hits the highlights of bioinformatics in the next two chapters. Chapter 2 describes methods and databases for finding sequences that are similar to a query sequence, with emphasis on BLAST at the National Center for Biotechnology Information. The algorithm and parameters are described in enough detail to demystify BLAST; the careful reader will know quite a bit about how to obtain and interpret desired results. Figures containing screen shots are particularly helpful in following along with the description, though they are somewhat difficult to read. Unfortunately, a few misstatements about E-values may confuse the novice BLAST user. An E-value is misinterpreted as a P-value, when in fact these quantities are distinct. A typographical error compounds the confusion between expectation and probability by describing an E-value of 0.02 as implying a 20% chance of obtaining the corresponding alignment score by chance (rather than 1-e Ϫ0.02 , or approximately 2% chance). The author's choice to explain global and local alignment algorithms is surprising but welcome-too many biologists never know about these more exact methods for sequence comparison. Chapter 3 focuses on protein sequence analysis, in particular, the characterization of protein domains and families, and lists several important methods and databases. Holmes, however, did not cover other bioinformatics topics such as: • algorithms and tools for predicting RNA and protein structure • finding genes, binding sites, and other motifs in newly sequenced organisms • comparing whole genomes • inferring relationships among species in phylogenetic trees • analyzing genome-wide expression data • inferring genetic regulatory relationships in gene networks Chapter 4 discusses computational cell biology models in abstract terms to provide a foundation for subsequent chapters, but the foundation it provides is insufficient. For example, the statement "we know that the plot of the velocity versus substrate concentration of a Michaelis-Menten model
doi:10.1187/cbe.08-06-0034 pmcid:PMC2527975 fatcat:vw37blinxvgtbom4e3x6akldou