Stochastic Modelling for Systems Biology, Third Edition

"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the author's webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering."

• Chitaranjan Mahapatra, Appeared in ISCB News, January 2020

Autorentext

Darren Wilkinson is Professor of Stochastic modelling at Newcastle University in the United Kingdom. He was educated at the nearby University of Durham, where he took his first degree in Mathematics followed by a PhD in Bayesian statistics which he completed in 1995. He moved to a Lectureship in Statistics at Newcastle University in 1996, where he has remained since, being promoted to his current post in 2007. Professor Wilkinson is interested in computational statistics and Bayesian inference and in the application of modern statistical technology to problems in statistical bioinformatics and systems biology. He is also interested in some of the big data challenges that arise in bioscience and more generally. He serves on Biotechnology and Biological Sciences Research Council's Strategy Advisory Panel for Exploiting new ways of working and is co-Director of Newcastle's Engineering and Physical Sciences Research Council Centre for Doctoral Training in Cloud Computing for Big Data. He is also a Fellow of the Alan Turing Institute for data science and artificial intelligence.

Zusammenfassung

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book.

New in the Third Edition

• New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation

• Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC

• Updated R package, including code relating to all of the new material

• New R package for parsing SBML models into simulatable stochastic Petri net models

• New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Inhalt

Introduction to biological modelling

Representation of biochemical networks

Probability models

Stochastic simulation

Markov processes

Chemical and biochemical kinetics

Case studies

Beyond the Gillespie algorithm

Spatially extended systems

Bayesian inference and MCMC

Inference for stochastic kinetic models

Conclusions

Appendices