The book is divided into twelve chapters plus appendices. The book also contains lists of acronyms, genes, software and databases, as well as a glossary and a very extensive bibliography. The introduction gives a nice overview cancer, system biology and why systems biology approaches are necessary for medicine in the treatment of cancer.
Chapter 2 and Appendix 1 introduce readers, with limited biological knowledge, to molecular biology from gene expression to epigenetics and signal transduction. Some parts of the appendix are written in the style of an extensive glossary. A specificity of cancer biology is the importance of mutations. These are described in detail, both at the genetic level as well as at the protein level, i.e. how modified proteins affect cellular regulation underlying the systems aspect of cancer.
Chapter 3 describes nicely and in an easily understandable manner high-throughput experimental techniques for the quantification of DNA, RNA, proteins and their interactions. Additionally, computational quantification methods are also presented. Some of these would be more easily understandable if a short motivation was added, e.g. for the denominator of BAF.
Chapter 4 gives an overview of bioinformatics tools and standards and seems to be written for a more theoretical audience. This applies to Section 4.1 in particular, due to its mathematical notation.
Chapter 5 discusses how features can be extracted from large-scale data, e.g. genome-wide mRNA or protein amount. Different methods are presented and well illustrated. These analyses are a key step between the high-throughput experiments and their medical interpretation. The mathematical foundation of the latter is thoroughly covered in Chapter 6. Methods of statistical inference for feature detection, for example, are presented.
Chapter 7 gives a good overview of dynamical mathematical modelling approaches. The role of positive and negative feedback loops are very clearly presented in the example of small cell cycle models. Chapter 8 gives a very detailed overview of published dynamical models involved in cancers, from growth and death signalling to energy metabolism.
Robustness is the topic of Chapters 9 and 10, in the first chapter from a biological point of view, in the second one from a mathematical one. Chapter 11 presents approaches targeting the fragility of networks and how these can be used for predicting drug targets.