Computational Modeling Of Gene Regulatory Networks -- A Primer Review

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(More customer reviews)There are quite a few books about systems biology and gene regulatory networks, most of which are very disappointing. I find only books written by scientists who are really working in this field are good. If you are not sure about one book, check the author's publications!
On the way to the library to borrow this book, I was thinking, among ~10 books I have read, I would only recommend Eric Davidson's book (biology aspects) and Uri Alon's book (mathematics aspects). After briefly reading this one, I think this book would also be on my recommendation list as a practical guide. It is very funny to find that this author also recommended those two books.
This book has three parts: the first is the introduction of modeling (Ch1-5); the second is about various models of regulation (Ch6-12), including implicit models, single-cell stochastic models, mass-action kinetics models, boolean models, Bayesian models, etc; the last part is about misc aspects of modeling (Ch13-22). They are well organized and coherent. The text is clear and easy to understand (I am not a native speaker), and the book is written for those who are suffering from math-phobia. The best part is, there are many links to free softwares and examples codes of models, so that you can play these models immediately. That's why I take this book as a practical guide in my recommendation list.
A complaint: figures have no numbers and legends; formulas look amateur. I understand that the author doesn't want to intimidate common readers. But these annoy me a little bit.
In a summary, a must-have book for systems biology and gene regulatory networks.


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This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.
Contents: Introduction; What Is a System, and Why Should We Care?; What Models Can and Cannot Predict; Why Make Computational Models of Gene Regulatory Networks?; Graphical Representations of Gene Regulatory Networks; Implicit Modeling via Interaction Network Maps; The Biochemical Basis of Gene Regulation; A Single-Cell Model of Transcriptional Regulation; Simplified Models: Mass-Action Kinetics; Simplified Models: Boolean and Multi-valued Logic; Simplified Models: Bayesian Networks; The Relationship between Logic and Bayesian Networks; Network Inference in Practice; Searching DNA Sequences for Transcription Factor Binding Sites; Model Selection Theory; Simplified Models -- GRN State Signatures in Data; System Dynamics; Robustness Analysis; GRN Modules and Building Blocks; Notes on Data Processing for GRN Modeling; Applications of Computational GRN Modeling; Quo Vadis.

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