Stochastic modelling for systems biology

著者

書誌事項

Stochastic modelling for systems biology

Darren J. Wilkinson

（Chapman and Hall/CRC mathematical & computational biology series / series editors Alison M. Etheridge ... [et al.]）（A Chapman & Hall book）

CRC Press/Taylor & Francis, 2012

2nd ed

  • : hardback

大学図書館所蔵 件 / 全5件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 323-330) and index

内容説明・目次

内容説明

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.

目次

Modelling and Networks Introduction to Biological Modelling What is modelling? Aims of modelling Why is stochastic modelling necessary? Chemical reactions Modelling genetic and biochemical networks Modelling higher-level systems Representation of Biochemical Networks Coupled chemical reactions Graphical representations Petri nets Stochastic process algebras Systems Biology Markup Language (SBML) SBML-shorthand Stochastic Processes and Simulation Probability Models Probability Discrete probability models The discrete uniform distribution The binomial distribution The geometric distribution The Poisson distribution Continuous probability models The uniform distribution The exponential distribution The normal/Gaussian distribution The gamma distribution Quantifying "noise" Stochastic Simulation Introduction Monte Carlo integration Uniform random number generation Transformation methods Lookup methods Rejection samplers Importance resampling The Poisson process Using the statistical programming language, R Analysis of simulation output Markov Processes Introduction Finite discrete time Markov chains Markov chains with continuous state-space Markov chains in continuous time Diffusion processes Stochastic Chemical Kinetics Chemical and Biochemical Kinetics Classical continuous deterministic chemical kinetics Molecular approach to kinetics Mass-action stochastic kinetics The Gillespie algorithm Stochastic Petri nets (SPNs) Structuring stochastic simulation codes Rate constant conversion Kolmogorov's equations and other analytic representations Software for simulating stochastic kinetic networks Case Studies Introduction Dimerisation kinetics Michaelis-Menten enzyme kinetics An auto-regulatory genetic network The lac operon Beyond the Gillespie Algorithm Introduction Exact simulation methods Approximate simulation strategies Hybrid simulation strategies Bayesian Inference Bayesian Inference and MCMC Likelihood and Bayesian inference The Gibbs sampler The Metropolis-Hastings algorithm Hybrid MCMC schemes Metropolis-Hastings algorithms for Bayesian inference Bayesian inference for latent variable models Alternatives to MCMC Inference for Stochastic Kinetic Models Introduction Inference given complete data Discrete-time observations of the system state Diffusion approximations for inference Likelihood-free methods Network inference and model comparison Conclusions SBML Models Auto-regulatory network Lotka-Volterra reaction system Dimerisation-kinetics model References Index All chapters include exercises and further reading.

「Nielsen BookData」 より

関連文献： 2件中  1-2を表示

詳細情報

ページトップへ