Stochastic Modelling for Systems Biology

Front Cover
CRC Press, Nov 9, 2011 - Mathematics - 363 pages

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context.

Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership.

New in the Second Edition

  • All examples have been updated to Systems Biology Markup Language Level 3
  • All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way
  • An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package
  • More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers
  • Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation
  • Simple modelling of "extrinsic" and "intrinsic" noise

An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

 

Selected pages

Contents

II Stochastic processes and simulation
49
III Stochastic chemical kinetics
169
IV Bayesian inference
247
SBMLModels
315
References
323
Copyright

Other editions - View all

Stochastic Modelling for Systems Biology, Second Edition
Darren J. Wilkinson
Limited preview - 2011

Common terms and phrases

approximation auto-regulatory Bayesian inference biochemical networks cell Chapter chemical compute consider context continuous deterministic corresponding defined denoted density diffusion process dimerisation kinetics dynamics equation example finite function gene Gibbs sampler Gillespie algorithm given in Figure hazard independent Initialise integration interval kineticLaw likelihood linear listOfLocalParameters listOfProducts listOfReactants Lotka–Volterra marginal marginal likelihood Markov chain Markov process matrix MCMC MCMC algorithm method Metropolis–Hastings multivariate noise normal random Note number of molecules parameters particle filter Petri plot Poisson process posterior distribution probability problem Proposition random quantity random variables rate constants rate laws RNAP sample path sample space SBML SBML-shorthand Section simple simulated realisation solution species speciesReference standard deviation stationary distribution stochastic kinetic models stochastic process stochastic simulation stoichiometry="1 straightforward systems biology techniques Theorem time-change tion transcription transition kernel update Var(X vector Wilkinson zero

About the author (2011)

Darren Wilkinson is Professor of Stochastic Modelling at Newcastle University in the UK. He was educated at the nearby University of Durham, where he took his first degree in Mathematics, followed by a Ph.D. in Bayesian statistics which he completed in 1995. He moved to a lectureship in statistics at the Newcastle University in 1996, where he has remained since, being promoted to his current post in 2007. Professor Wilkinson is interested in computational statistics and Bayesian inference and in the application of modern statistical technology to problems in statistical bioinformatics and systems biology. He is involved in a variety of systems biology projects at Newcastle, including the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN). He recently held a BBSRC Research Development Fellowship on Integrative modelling of stochasticity, noise, heterogeneity and measurement error in the study of model biological systems.

Bibliographic information

TitleStochastic Modelling for Systems Biology
Chapman & Hall/CRC Mathematical and Computational Biology
AuthorDarren J. Wilkinson
Edition2, illustrated
PublisherCRC Press, 2011
ISBN1439837767, 9781439837764
Length363 pages
Subjects ›  › 

Mathematics / Probability & Statistics / General
Science / Biotechnology
Science / Life Sciences / Biology
  
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