Handbook of Statistical Systems Biology

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John Wiley & Sons, 09.09.2011 - 530 Seiten
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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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Inhalt

Two Challenges of Systems Biology
Introduction to Statistical Methods for Complex
Bayesian Inference and Computation
Data Integration Towards Understanding
Control Engineering Approaches toReverse Engineering Biomolecular Networks
Transcriptomic Technologies and Statistical Data
StatisticalData Analysis inMetabolomics 8 1 Introduction
ImagingandSingleCell Measurement
Chapter
References
Stochastic Dynamical Systems
Gaussian Process Inferencefor Differential Equation Models of Transcriptional Regulation
Model Identification by Utilizing Likelihood
1ODE Models forReaction Networks 20 2 Parameter Estimation
Acknowledgements
Inferenceand ModelSelectionforDynamical

References
Introduction to Graphical Modelling
Recovering Genetic Network from Continuous
Advanced Applications of Bayesian Networks
RandomGraphModels
3PPI network models 14 4Rangedependent graphs
Chapter
Chapter
HostPathogen Systems Biology
Bayesian Approaches for Based Metabolomics Mass Spectrometry
Systems Biologyof microRNAs 25 1 Introduction
Index
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