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.Read more
- Provides a practical example-based introduction
- Includes tried and tested material developed by the authors at the University of Oxford
- A suitable course text for advanced undergraduate and beginning graduate students in applied mathematics
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- Date Published: January 2020
- format: Paperback
- isbn: 9781108703000
- dimensions: 228 x 152 x 13 mm
- weight: 0.45kg
- contains: 8 b/w illus. 86 colour illus. 3 tables 88 exercises
- availability: In stock
Table of Contents
1. Stochastic simulation of chemical reactions
2. Deterministic versus stochastic modelling
3. Stochastic differential equations
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
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