Handbook of Statistical Systems Biology

About this book

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.

Contents

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

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