Michael Stumpf, David J. Balding, Mark Girolami

Handbook of Statistical Systems Biology (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 530
• Erscheinungstermin: 9. September 2011
• Englisch
• ISBN-13: 9781119952046
• Artikelnr.: 37338658

Autorenporträt

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

Inhaltsangabe

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.