Michael Stumpf & David J. Balding 
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.

Table of Content

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 micro RNA.

About the author

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