Handbook of Statistical Systems Biology
by Stumpf, Michael; Balding, David J.; Girolami, MarkEdition: 1st
Format: Hardcover
Pub. Date: 2011-10-17
Publisher(s): Wiley
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Summary
Systems Biology is moving away from the mathematical modeling and subsequent analysis of systems to a full statistical analysis and probabilistic reasoning over the inferences that can be made from these modeling exercises. There is a need for a comprehensive handbook covering the major themes in the area. This book provides a full and detailed treatment of important and emerging fields of statistical systems biology, with focus on characterising uncertainty and stochastic effects in mathematical models of biological systems.
Author Biography
Michael Stumpf, Theoretical Systems Biology at Imperial College London
David Balding, Statistical Genetics in the Institute of Genetics at University College London
Mark Girolami, Department of Computing Science and the Department of Statistics
Table of 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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