Handbook of Statistical Systems Biology

Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Chapter 1 Two challenges of systems biology. Chapter 2 Introduction to Statistical Methods for Complex Systems. Chapter 3 Bayesian Inference and Computation. Chapter 4 Data Integration: Towards Understanding Biological Complexity. Chapter 5 Control Engineering Approaches to Reverse Engineering Biomolecular Approaches. Chapter 6 Algebraic Statistics and Methods in Systems Biology. B. Technology-based Chapters. Chapter 7 Transcriptomic Technologies and Statistical Data Analysis. Chapter 8 Statistical Data Analysis in Metabolomics. Chaper 9 Imaging and Single-Cell Measurement Technologies. Chapter 10 Protein Interaction Networks and Their Statistical Analysis. C. Networks and Graphical Models. Chapter 11 Introduction to Graphical Modelling. Chapter 12 Recovering Genetic Network from Continuous Data with Dynamic Bayesian Networks. Chapter 13 Advanced Applications of Bayesian Networks in Systems Biology. Chapter 14 Random Graph Models and Their Application to Protein-Protein Interaction Networks. Chapter 15 Modelling Biological Networks Via Tailored Random Graphs. D. Dynamical Systems. Chapter 16 Nonlinear Dynamics: a Brief Introduction. Chapter 17 Qualitative Inference for Dynamical Systems. Chapter 18 Stochastic Dynamical Systems. Chapter 19 State-Space models. Chapter 20 Model Identification by Utilizing Likelihood-Based Methods. E. Application Areas. Chapter 21 Inference of Signalling Pathway Models. Chapter 22 Modelling Transcription Factor Activity. Chapter 23 Host-Pathogen Systems Biology. Chapter 24 Statistical Metabolomics: Bayesian Challenges in the Analysis of Metabolomic Data. Chapter 25 Systems Biology of microRNA.