This is the second edition of a blockbuster devoted to the modeling of biomolecular structure and dynamics. It is part of Springer’s Interdisciplinary Applied Mathematics series, and it fits the criteria of that series very well. This is an area that is developing very quickly, and the book considers great swaths of it. That means that few subjects can be covered in great detail, but it allows the author to convey a global view of fascinating problems and intriguing approaches. The book addresses a broad audience that includes mathematicians, computer scientists, physical scientists, and biologists. It is infused with a sense of the excitement of a young field with frequent new developments.
The book includes overviews of three broad topics. These are:
The first two chapters provide a historical perspective for biomolecular modeling. Here the author discusses progress in experimental techniques, challenges arising in computation, and some of the practical applications in the treatment of disease and understanding the genome.
The best place for mathematicians (or others without some background in the area) to start is Chapters 3 and 4, where the author reviews basic elements of protein structure. If you’re not sure you know what a peptide is, for example, best to skip around in the first chapter and head quickly to Chapter 3. (Skipping around is generally a good strategy with this book. It is somewhat nonlinear, and it would be a mistake to be stymied by hitting a stumbling block early on.) Chapters 5 and 6 provide a mini-tutorial on nucleic acids.
The second part of the book focuses on molecular mechanics and force fields. This puts molecular mechanics into context as an offspring of quantum mechanics, and then dives into a discussion of the several potential energy functions of particular interest in chemistry. The third part deals with molecular dynamics and the associated simulations that embody a computational approach to statistical mechanics. The molecular dynamics approach is appealing because it aligns with physical intuition, and because it is relatively simple. The text also describes alternate approaches, including Monte Carlo simulation and Poisson-Boltzmann analysis, energy minimization, Brownian dynamics and enhanced sampling methods.
The book is designed for beginning graduate students in a variety of disciplines. According to the author, it evolved from a course on molecular modeling at New York University. Although many parts are accessible to anyone with reasonable training in the sciences, some previous exposure to biochemistry would be quite helpful. The author says, “Ideally, a good grounding in basic biochemistry, chemical physics, statistical and quantum mechanics, scientific programming (i.e., numerical methods) and programming techniques is desired. The rarity of such a background required me to offer tutorials in both biological and mathematical areas.”
The appendices include a huge bibliography, a sample course syllabus, supplementary course texts (especially useful to help fill in gaps in the text), and a variety of innovative homework assignments.
Bill Satzer (email@example.com) is a senior intellectual property scientist at 3M Company, having previously been a lab manager at 3M for composites and electromagnetic materials. His training is in dynamical systems and particularly celestial mechanics; his current interests are broadly in applied mathematics and the teaching of mathematics.