Following the introduction, there are two framing chapters, the first on data analysis and handling and the second on using the Internet; these are written for the biochemist but draw on general resources. From there, the book jumps into molecular modeling, enzyme kinetics, metabolism, proteomics and genomics, and finally structure prediction. Tsai has an admirably global view of the ways in which computers contribute to modern biochemistry and, to my eye, has cast his net wide enough to capture a good fraction of what’s going on out there. The trouble is that his catch is just too large for a book of this modest length to comfortably contain, given its stated mission of presenting the material to biochemistry students.

Each chapter is economically written, with a background section followed by an introduction to the computer resources at hand (often including descriptions of databases and software available to analyze these data), and then some step-by-step examples of how to use the resources. A set of “workshops” follows each chapter, allowing the reader to test his or her ability to extend the chapter’s models independently. The chief difficulty in this structure is the brevity of the introductory material. In many instances, substantial familiarity is assumed with material that does not generally enter the undergraduate curriculum. For example, in the chapter on enzyme kinetics, there is a presentation of the steady state assumption applied to multi-substrate reactions, invoking all microscopic rate constants. While this topic isn’t beyond the reach of a student who has completed a year of biochemistry, it is an imposing treatment that receives only a portion of the six pages devoted to the basics of enzyme kinetics. A better approach might have been to focus on simpler kinetic behavior, rather than complicating the process of learning to use databases and modeling software with the process of learning more advanced topics in enzyme kinetics.

Similar problems exist for the sections on receptor–ligand interactions, metabolic fluxes, molecular mechanics, and phylogeny constructions. These treatments are sufficient for a researcher in the field, or a student enrolled in a class devoted to one of these topics, but otherwise I find it difficult to see how a student would simultaneously learn both the science and the techniques that are covered here without substantial time commitment. Tsai appears to have succeeded in his home institution, but I suspect many instructors at undergraduate schools would have a hard time finding a suitable audience for this material.

That said, the book does have substantial value for active biochemists. I was struck by how many of the general biochemical databases Tsai lists in Chapter 3 were unknown to me. Even more impressive, all of them are still on the Web in August 2002 following an April 2002 publication date (though some of the links are beginning to change). Each chapter is a treasure chest of databases, software, and opportunities, and all of these are catalogued in a remarkable, extensive appendix. Even where I had considerable experience before reading his chapters (particularly in molecular modeling and sequence analysis), I still picked up a number of useful tools. Tsai is also generally careful to identify software that can be downloaded for free (like BioKin and IsisDraw) or commonly available packages (like Microsoft Office and HyperChem). Undoubtedly there will be some software incompatibilities for any given user. Someone who has already invested in SigmaPlot can skip the section on using Excel for linear regressions. Also, a Mac user (which I am about 80% of the time) will not be able to use several packages, for instance Microsoft Access and Leonora (for analyzing enzyme kinetics data). In addition, even when I had access to the software Tsai describes, I found some instructions difficult to follow due to differences in release versions. But, overall, I found that just being told that a task could be performed was sufficient to drive my interest in seeing it happen. Of course, in all of the topics presented in this text, the reader needs to be aware of the “garbage in/garbage out” phenomenon. Tsai doesn’t go into any detail in evaluating, for example, which force field should be used in a particular molecular mechanics calculation, so the cautious reader will be sure to read software manuals and the recommended texts to get the full, and necessary, background.

At $70 in a paperback binding, this is not an inexpensive book, and given its topic, it’s not likely to be durable in a single edition, but it’s a worthwhile addition to a personal bookshelf or an institution’s library for someone interested in expanding his or her vision of how the study of biochemistry can be enhanced by computational resources.