SMfSB / 2nd edition
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.
Note that there is now a third edition of the book, which contains new material and other updates.
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
- Web links
Chapter 2: Representation of biochemical networks
- Code
- Full SBML for the example auto-regulatory network (you might have to right-click on this and "save as", depending on your browser)
- SBML-shorthand for the auto-regulatory network
- Software
- Web links
- Petri Nets World
- Bio-PEPA
- SBML.org
- SBML Documents (specifications, etc.)
- BioModels database
- XML at CoverPages
- MathML
- Web links
Chapter 4: Stochastic simulation
- Code
- mytable.txt (from the R mini-tutorial)
- Software
- Web links
- Introduction to R and Bioconductor
- e-book: L. Devroye (1986) Non-Uniform Random Variate Generation
- e-book: J. E. Gentle, W. Hardle and Y. Mori (2004) Handbook of Computational Statistics
- Code
- The main R functions used in this chapter: fmc, cfmc, imdeath and rdiff, are included (and documented) in the smfsb R package described above
- Gene activation example (SBML, SBML-shorthand)
- Immigration-death example (SBML, SBML-shorthand)
- Web links
Chapter 6: Chemical and biochemical kinetics
- Code
- The main R functions used in this chapter: simpleEuler, gillespie, discretise, gillespied, StepGillespie, simTs, simSample, simTimes and StepEulerSPN are included (and documented) in the smfsb R package described above
- Stochastic Lotka-Volterra example (SBML, SBML-shorthand)
- Software
- SBW - includes a "gillespie" simulation service
- COPASI - includes facilities for discrete stochastic simulation
- gillespie2 - a C implementation of a SBML Level 2 discrete stochastic simulator
- Web links
- SBML test suite for testing SBML-compliant continuous deterministic simulators
- SBML discrete stochastic model test suite for testing SBML-compliant stochastic simulators
- Code
- Dimerisation kinetics model (continuous deterministic version) (SBML, SBML-shorthand)
- Dimerisation kinetics model (discrete stochastic version) (SBML, SBML-shorthand)
- 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)
Chapter 8: Beyond the Gillespie algorithm
- Code
- The main R functions used in this chapter: StepFRM, StepPTS, and StepCLE are included (and documented) in the smfsb R package described above
- Software
Chapter 9: Bayesian inference and MCMC
- Code
- The main R functions used in this chapter: normgibbs and metrop are included (and documented) in the smfsb R package described above
- Software
- Web links
- Bayesian inference and R (from CRAN)
Chapter 10: Inference for stochastic kinetic models
- Code
- The main R functions used in this chapter: pfMLLik, as.timedData and mcmcSummary are included (and documented) in the smfsb R package described above. A pMCMC demo can be run with demo("PMCMC").
- Web links
- Darren Wilkinson's publication list
- Web links
- Darren Wilkinson's research blog
- CaliBayes - project on Bayesian calibration of biological simulators
Errata
- Errata in PDF format
