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.
