Model identification by utilizing likelihood-based methods
- 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. ntroduces 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.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. ntroduces 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 ranscriptomic 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.…
Author: | Andreas RaueORCiDGND, J. Timmer |
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URN: | urn:nbn:de:bvb:384-opus4-1132704 |
Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/113270 |
ISBN: | 978-0-470-71086-9OPAC |
Parent Title (English): | Handbook of statistical systems biology |
Publisher: | Wiley |
Place of publication: | New York, NY |
Editor: | Michael Stumpf, David J. Balding, Mark Girolami |
Type: | Part of a Book |
Language: | English |
Date of Publication (online): | 2024/05/31 |
Year of first Publication: | 2011 |
Publishing Institution: | Universität Augsburg |
Release Date: | 2024/06/03 |
First Page: | 395 |
Last Page: | 416 |
DOI: | https://doi.org/10.1002/9781119970606.ch20 |
Institutes: | Fakultät für Angewandte Informatik |
Fakultät für Angewandte Informatik / Institut für Informatik | |
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Modellierung und Simulation biologischer Prozesse | |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Licence (German): | ![]() |