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:IntroductionWhat Is a System, and Why Should We Care?What Models Can and Cannot PredictWhy Make Computational Models of Gene Regulatory Networks?Graphical Representations of Gene Regulatory NetworksImplicit Modeling via Interaction Network MapsThe Biochemical Basis of Gene RegulationA Single-Cell Model of Transcriptional RegulationSimplified Models: Mass-Action KineticsSimplified Models: Boolean and Multi-valued LogicSimplified Models: Bayesian NetworksThe Relationship between Logic and Bayesian NetworksNetwork Inference in PracticeSearching DNA Sequences for Transcription Factor Binding SitesModel Selection TheorySimplified Models — GRN State Signatures in DataSystem DynamicsRobustness AnalysisGRN Modules and Building BlocksNotes on Data Processing for GRN ModelingApplications of Computational GRN ModelingQuo Vadis
Readership: Undergraduate- and graduate-level experimental biology students and researchers and practicing biologists; computational biology students and researchers; students and researchers in bioengineering, genome sciences, and related disciplines.
