Stochastic Modelling of Reaction-Diffusion Processes
Published on 30. January 2020
Book
Paperback/Softback
319 pages
978-1-108-70300-0 (ISBN)
Description
This practical introduction to stochastic reaction-diffusion modelling is based on courses taught at the University of Oxford. The authors discuss the essence of mathematical methods which appear (under different names) in a number of interdisciplinary scientific fields bridging mathematics and computations with biology and chemistry. The book can be used both for self-study and as a supporting text for advanced undergraduate or beginning graduate-level courses in applied mathematics. New mathematical approaches are explained using simple examples of biological models, which range in size from simulations of small biomolecules to groups of animals. The book starts with stochastic modelling of chemical reactions, introducing stochastic simulation algorithms and mathematical methods for analysis of stochastic models. Different stochastic spatio-temporal models are then studied, including models of diffusion and stochastic reaction-diffusion modelling. The methods covered include molecular dynamics, Brownian dynamics, velocity jump processes and compartment-based (lattice-based) models.
Reviews / Votes
'The text can be used effectively for solitary study or as a textbook for a course offered at the boundary between undergraduate and beginning graduate study ... This is a remarkable, even admirable, work that bears the mark of its Oxford origins. Its potential audience includes chemists and mathematicians as well as adventuresome biologists and physicists and perhaps even bright or intrepid general readers.' A. E. Viste, Choice 'This textbook is an example-driven introduction to stochastic modeling in mathematical biology ... Beyond serving as a course textbook, the book could serve as a good general introduction to the area of stochastic modeling in biology for researchers, particularly given the copious citations to more specialist texts.' Andrew Krause, MAA Reviews 'Erban and Chapman's Stochastic Modelling of Reaction-Diffusion Processes will be valuable both as a reference for practitioners and as a textbook for a graduate course on stochastic modelling. Every chapter includes problems for the reader. The problems are well written and appropriate for most intended readers of the book. I hope that this book is widely adopted and that it becomes a standard textbook in the field.' Michael A. Salins, Mathematical Reviews/MathSciNet Review 'This book is also available at a reduced price as an e-book on Kindle. Based on the sample I viewed, all the features of the printed book have been perfectly preserved, with no loss of clarity in the layout or the mathematical symbols or the graphs and diagrams.' David Hopkins, The Mathematical GazetteMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises; 3 Tables, black and white; 43 Plates, color; 26 Halftones, color; 8 Halftones, black and white; 17 Line drawings, color
Dimensions
Height: 226 mm
Width: 155 mm
Thickness: 13 mm
Weight
454 gr
ISBN-13
978-1-108-70300-0 (9781108703000)
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Schweitzer Classification
Other editions
Additional editions
Radek Erban | S. Jonathan Chapman
Stochastic Modelling of Reaction-Diffusion Processes
Book
01/2020
Cambridge University Press
€174.10
Shipment within 15-20 days
Radek Erban | S. Jonathan Chapman
Stochastic Modelling of Reaction-Diffusion Processes
E-Book
01/2020
Cambridge University Press
€40.99
Available for download
Persons
Radek Erban is Professor of Mathematics at the University of Oxford, a Fellow of Merton College, Oxford and a Royal Society University Research Fellow.
Content
1. Stochastic simulation of chemical reactions; 2. Deterministic versus stochastic modelling; 3. Stochastic differential equations; 4. Diffusion; 5. Efficient stochastic modelling of chemical reactions; 6. Stochastic reaction-diffusion models; 7. SSAs for reaction-diffusion-advection processes; 8. Microscopic models of Brownian motion; 9. Multiscale and multi-resolution methods; Appendix A. Deterministic modelling of chemical reactions; Appendix B. Discrete probability distributions; Appendix C. Continuous probability distributions; References; Index.