ChapterNotes.md

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

• Full SBML for the example auto-regulatory network
• SBML-shorthand for the auto-regulatory network

Software

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

Web links

• Petri Nets World
• Bio-PEPA
• SBML.org
• SBML Documents (specifications, etc.)
• BioModels database
• XML at CoverPages
• MathML

Chapter 3: Probability models

Web links

• Wikipedia: Probability, probability theory, discrete probability distribution, continuous probability distribution, Poisson process
• Mathworld: Probability

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

• StochSS
• SynBioSS
• FERN

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

• MesoRD
• Smoldyn
• StochSim

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