Simple Brownian Diffusion
An Introduction to the Standard Theoretical Models
Nonfiction, Science & Nature, Science, Chemistry, Physical & Theoretical, Physics, Mathematical Physics
| Author: | Daniel Thomas Gillespie, Effrosyni Seitaridou | ISBN: | 9780191641534 |
| Publisher: | OUP Oxford | Publication: | October 18, 2012 |
| Imprint: | OUP Oxford | Language: | English |
| Author: | Daniel Thomas Gillespie, Effrosyni Seitaridou |
| ISBN: | 9780191641534 |
| Publisher: | OUP Oxford |
| Publication: | October 18, 2012 |
| Imprint: | OUP Oxford |
| Language: | English |
Brownian diffusion is the motion of one or more solute molecules in a sea of very many, much smaller solvent molecules. Its importance today owes mainly to cellular chemistry, since Brownian diffusion is one of the ways in which key reactant molecules move about inside a living cell. This book focuses on the four simplest models of Brownian diffusion: the classical Fickian model, the Einstein model, the discrete-stochastic (cell-jumping) model, and the Langevin model. The authors carefully develop the theories underlying these models, assess their relative advantages, and clarify their conditions of applicability. Special attention is given to the stochastic simulation of diffusion, and to showing how simulation can complement theory and experiment. Two self-contained tutorial chapters, one on the mathematics of random variables and the other on the mathematics of continuous Markov processes (stochastic differential equations), make the book accessible to researchers from a broad spectrum of technical backgrounds.
Brownian diffusion is the motion of one or more solute molecules in a sea of very many, much smaller solvent molecules. Its importance today owes mainly to cellular chemistry, since Brownian diffusion is one of the ways in which key reactant molecules move about inside a living cell. This book focuses on the four simplest models of Brownian diffusion: the classical Fickian model, the Einstein model, the discrete-stochastic (cell-jumping) model, and the Langevin model. The authors carefully develop the theories underlying these models, assess their relative advantages, and clarify their conditions of applicability. Special attention is given to the stochastic simulation of diffusion, and to showing how simulation can complement theory and experiment. Two self-contained tutorial chapters, one on the mathematics of random variables and the other on the mathematics of continuous Markov processes (stochastic differential equations), make the book accessible to researchers from a broad spectrum of technical backgrounds.
