Prof Darren Wilkinson Professor of Stochastic Modelling
School of Mathematics & Statistics
Newcastle University

SMfSB / 2nd edition

SMfSB2e

Stochastic Modelling for Systems Biology, second edition

This page contains links, code snippets, software and other information relating to the second edition of my book, Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC, November 2011.

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 143983772-4 and ISBN-13 978-143983772-6. It can be ordered from CRC Press, Amazon.com, Amazon.co.uk and similar book sellers. You can also get a preview of the book on google books (or using Amazon Look Inside).

New in the second edition: R code updated, extended and properly documented in a free open source R package. All SBML models updated to SBML Level 3. Brief introduction to stochastic process algebras. Greatly extended chapter on Markov processes, with additional emphasis on stochastic differential equations and analytical approaches. Introduction to Kurtz's random time change representation, and informal derivations of Ito's formula, the Kolmogorov equations, the Fokker-Planck equation and the linear noise approximation. New R code for exact and approximate stochastic simulation, using a modular, functional programming approach. Quantification of noise, and an introduction to the modelling of extrinsic as well as intrinsic noise. Extended material on inference for stochastic models from data, covering "likelihood free" and "particle MCMC" approaches. Example code for particle filtering, marginal likelihood estimation and particle MCMC. Updated references and end of chapter exercises.


Getting started with R and the R package, smfsb

R is free software, available for all major operating systems, and can be obtained from the R project website (or a mirror). The R code associated with this book is freely available as an R package, which is maintained on R-Forge and distributed via CRAN. It should be possible to install it by entering the following command at the R command prompt:

install.packages("smfsb")
On platforms where binary package installs are the default (eg. Windows), the binary package may not install on older versions of R - if installation fails, try updating R to the latest version, or find out about installing packages from source. The R-Forge project has title "SMfSB 2e" and name "smfsb" - see the package web home pages on R-Forge and CRAN for further details. The package does work on all major operating systems (Linux, Windows, Mac, ...). Please try consulting the available information on CRAN and R-Forge before emailing me about installation problems.

Once the smfsb package is installed, it can be loaded with

library(smfsb)
and an overview vignette can be accessed by using the command
vignette("smfsb",package="smfsb")
There should be sufficient information provided in the vignette in order to get started with using the package. Also note that I have written a blog post providing a tutorial introduction to the smfsb package, based closely on the introductory vignette.


Code, software and other web links, by chapter

Chapter 1: Introduction to biological modelling

Chapter 2: Representation of biochemical networks

Chapter 3: Probability models

Chapter 4: Stochastic simulation

Chapter 5: Markov processes

Chapter 6: Chemical and biochemical kinetics

Chapter 7: Case studies

Chapter 8: Beyond the Gillespie algorithm

Chapter 9: Bayesian inference and MCMC

Chapter 10: Inference for stochastic kinetic models

Chapter 11: Conclusions

Errata


Please inform me immediately if you find any broken link on this page.

Darren J Wilkinson Book: Stochastic Modelling for
	      Systems Biology darren.wilkinson@ncl.ac.uk
http://www.staff.ncl.ac.uk/d.j.wilkinson/
tinyurl.com/darrenjw

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