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Chapter notes

Code, software and other web links, by chapter

Chapter 1: Introduction to biological modelling

Web links

Wikipedia: Systems biology, birth-death process, Markov process, transcription, translation, gene regulation, chemical reaction, elementary reaction, stoichiometry, lac operon, sigma factor, ubiquitin, epidemic model, Lotka-Volterra

Chapter 2: Representation of biochemical networks

Code

Software

CellDesigner
COPASI
SBML-shorthand
libSBML - library for parsing and generating SBML
SPiM

Web links

Chapter 3: Probability models

Web links

Chapter 4: Stochastic simulation

Code

mytable.txt (from the R mini-tutorial)

Software

R Project
CRAN (Comprehensive R Archive Network)
R Manuals
RStudio

Web links

Introduction to R and Bioconductor
e-book: L. Devroye (1986) Non-Uniform Random Variate Generation
Wikipedia: Linear congruential generator

Chapter 5: Markov processes

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

Wikipedia: stochastic process, Markov chain, Markov process, Ito diffusion

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

smfsbSBML R package for parsing SBML models into simulatable stochastic Petri nets
COPASI - includes facilities for discrete stochastic simulation
scala-smfsb - Scala library re-implementing the functionality of the smfsb R package in a fast, efficient, compiled, strongly typed functional programming language

Web links

SBML test suite for testing SBML-compliant continuous deterministic simulators. Now also includes the Discrete stochastic models test suite, for testing stochastic simulators
Wikipedia: Runge Kutta

Chapter 7: Case studies

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: Spatially extended systems

Code

The main R functions used in this chapter: StepGillespie1D, simTs1D, StepCLE1D, StepGillespie2D, simTs2D, StepCLE2D are included (and documented) in the smfsb R package described above

Software

Chapter 10: Bayesian inference and MCMC

Code

The main R functions used in this chapter: normgibbs, metrop, metropolisHastings and abcRun are included (and documented) in the smfsb R package described above

Software

JAGS (rjags - R interface to JAGS)
Stan (rstan)
OpenBugs
WinBUGS

Web links

Bayesian inference and R (Task view from CRAN)

Chapter 11: Inference for stochastic kinetic models

Code

The main R functions used in this chapter: pfMLLik, as.timedData, metropolisHastings, mcmcSummary, abcRun and abcSmc are included (and documented) in the smfsb R package described above. A pMCMC demo can be run with demo("PMCMC").

Software

scala-smfsb - Scala library re-implementing the functionality of the smfsb R package in a fast, efficient, compiled, strongly typed functional programming language - includes inference algorithms

Web links

Darren Wilkinson's publication list

Chapter 12: Conclusions

Web links

Darren Wilkinson's blog