SMfSB / 1st edition

Stochastic Modelling for Systems Biology

This page contains links, code snippets, software and other information relating to my book, Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC on 18th April 2006.

Note that this is the page for the first edition of the book (published 2006). There is now also a separate page for the second edition, published November 2011. The page for the second edition contains details of the R package smfsb, which is likely to be of interest to owners of the first edition. Note that there is now also a third edition of the book, published in 2018. If you still are still using the first edition, you should seriously consider upgrading to the new edition.

Quick jump to Chapter 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, Errata.

Information about the book

The book has ISBN-10 1-58488-540-8 and ISBN-13 978-158488-540-5. You can find out more details about it from the CRC book listing. It is Volume 11 in the Mathematical and Computational Biology series. You can buy the book direct from the publisher's site, but it is also listed on Amazon.com, Amazon.co.uk and Amazon.de if you would prefer to buy your copy from there. It is also listed on google books if you want a preview.

A brief description from the CRC book listing:

"Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. Stochastic Modeling for Systems Biology provides an accessible introduction to this theory using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modeling biological systems. Along with the latest simulation techniques and research material, such as parameter inference, the text includes many examples and figures as well as software code in R for various applications."

Independent reviews of the book

The Royal Statistical Society, by John Haigh
BioMedical Engineering Online, by Eric Bullinger
Briefings in Bioinformatics, by Russell Schwartz

Code, software and other web links

Chapter 4: Stochastic simulation

Code

mytable.txt (from the R mini-tutorial)

Software

R Project
CRAN (Comprehensive R Archive Network)

Web links

Quick introduction to R

Chapter 5: Markov processes

Code

Finite Markov chains in R - function and example
Continuous time finite Markov chains in R - function and example
Immigration-death process in R - function and example
Simulation of a diffusion process in R - function and example
Gene activation example (SBML, SBML-shorthand)
Immigration-death example (SBML, SBML-shorthand)

Web links

Wikipedia: stochastic process

Chapter 6: Chemical and biochemical kinetics

Code

Euler method in R - function and example
Simple Gillespie algorithm for a fixed number of events in R - functions and example
Gillespie algorithm for a fixed time period on a regular time grid in R - function and example
Stochastic Lotka-Volterra example (SBML, SBML-shorthand)

Software

gillespie2 SBML Level 2 discrete stochastic simulator
Dizzy stochastic simulator

Web links

SBML semantic validation suite for testing SBML-compliant continuous deterministic simulators
SBML discrete stochastic model test suite for testing SBML-compliant stochastic simulators

Chapter 7: Case studies

Code

Dimerisation kinetics model (continuous deterministic version) (SBML, SBML-shorthand)
Dimerisation kinetics model (discrete stochastic version) (SBML, SBML-shorthand, R SPN object)
Michaelis-Menten kinetics model (continuous deterministic version) (SBML, SBML-shorthand)
Michaelis-Menten kinetics model (discrete stochastic version) (SBML, SBML-shorthand)
Dimension-reduced Michaelis-Menten kinetics model (discrete stochastic version) (SBML, SBML-shorthand)
lac operon model (SBML, SBML-shorthand)