Stumpf / Balding / Girolami Handbook of Statistical Systems Biology

1. Auflage 2011
ISBN: 978-1-119-95204-6
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 530 Seiten, E-Book

Systems Biology is now entering a mature phase in which the keyissues are characterising uncertainty and stochastic effects inmathematical models of biological systems. The area is movingtowards a full statistical analysis and probabilistic reasoningover the inferences that can be made from mathematical models. Thishandbook presents a comprehensive guide to the discipline forpractitioners and educators, in providing a full and detailedtreatment of these important and emerging subjects. Leading expertsin systems biology and statistics have come together to provideinsight in to the major ideas in the field, and in particularmethods of specifying and fitting models, and estimating theunknown parameters.
This book:
* Provides a comprehensive account of inference techniques insystems biology.
* Introduces classical and Bayesian statistical methods forcomplex systems.
* Explores networks and graphical modeling as well as a widerange 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 engineeringapproaches.
* Provides colour illustrations to explain key concepts.
This handbook will be a key resource for researchers practisingsystems biology, and those requiring a comprehensive overview ofthis important field.

Autoren/Hrsg.

Stumpf, Michael

Balding, David J.

Girolami, Mark

Inhaltsverzeichnis

Chapter 1 Two challenges of systems biology.
Chapter 2 Introduction to Statistical Methods for ComplexSystems.
Chapter 3 Bayesian Inference and Computation.
Chapter 4 Data Integration: Towards Understanding BiologicalComplexity.
Chapter 5 Control Engineering Approaches to Reverse EngineeringBiomolecular Approaches.
Chapter 6 Algebraic Statistics and Methods in SystemsBiology.
B. Technology-based Chapters.
Chapter 7 Transcriptomic Technologies and Statistical DataAnalysis.
Chapter 8 Statistical Data Analysis in Metabolomics.
Chaper 9 Imaging and Single-Cell Measurement Technologies.
Chapter 10 Protein Interaction Networks and Their StatisticalAnalysis.
C. Networks and Graphical Models.
Chapter 11 Introduction to Graphical Modelling.
Chapter 12 Recovering Genetic Network from Continuous Data withDynamic Bayesian Networks.
Chapter 13 Advanced Applications of Bayesian Networks in SystemsBiology.
Chapter 14 Random Graph Models and Their Application toProtein-Protein Interaction Networks.
Chapter 15 Modelling Biological Networks Via Tailored RandomGraphs.
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-BasedMethods.
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 theAnalysis of Metabolomic Data.
Chapter 25 Systems Biology of microRNA.