Petri nets have been used extensively over the last 40 years to model a variety of domains, and their success can be attributed to the combination of a simple, intuitive graphical depiction overlaying a surprisingly powerful and robust semantic base. This volume brings together a variety of contributions showcasing the application of modeling biological and biochemical processes using Petri nets.

The contents are divided into three parts: “Foundations,” “Modeling Techniques,” and “Biochemical Applications.” One of the main strengths of the contributions is that they go beyond showing the syntax of the models, and actually explain the methods of analysis of the models that are available, both qualitative and quantitative. The applications being modeled are the usual suspects: the E. coli stress circuit, the MAPK signaling pathway, Lotka-Volterra dynamics, and so on. The models are extensively analyzed using the mathematical interpretation of variations of Petri nets, such as stochastic versions and continuously timed versions.

As a volume comprising so many diverse contributions, there are bound to be editing issues. Sometimes, concepts are used before being defined in a later chapter, and other times different interpretations are given by different authors without relating these interpretations. Some of these issues could be addressed by including a comprehensive glossary. While there is a glossary, it is quite incomplete and only covers simple concepts in Petri net theory. Cross-references between the chapters would also be useful.

The “Foundations” part is the least satisfying. For example, names and uniform resource locators (URLs) of data resources are introduced, but these are not discussed further in the following chapters. Furthermore, there is no explanation of how data from these repositories can be mapped into Petri net models. The “Biochemical Fundamentals” chapter is a nice introduction to Biology 101, but is not particularly focused on the domains of interest that appear in later chapters.

This raises the question of the target audience for the book. The editors suggest that it can be used as a teaching aid, and have included exercises at the end of each chapter. However, these exercises are of variable depth and the chapters do not cover the material in a way that emphasizes learning; they are not practical tutorials, and provide little guidance on how to build a model or use the tools mentioned. Rather, the volume is best seen as a roadmap to a variety of interesting uses of a modeling technique that is particularly geared to readers who already have some experience with mathematical modeling in biology.

While the text is understandable, poor grammar in many places detracts from readability. On the book’s Web site (http://pnbook.uni-frankfurt.de/), the editors do in fact welcome corrections from readers. The Web site also provides solutions to exercises.