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 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.

 

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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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Über den Autor (2011)

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

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