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Chapter notes

Code, software and other web links, by chapter

Chapter 1: Introduction to biological modelling

Web links

Chapter 2: Representation of biochemical networks

Code

Software

Web links

Chapter 3: Probability models

Web links

Chapter 4: Stochastic simulation

Code

Software

Web links

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

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

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

Web links

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

Chapter 12: Conclusions

Web links