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About this product
- Author(s)Darren J. Wilkinson
- PublisherTaylor & Francis Inc
- Date of Publication07/11/2011
- GenreLife Sciences: General
- Series TitleChapman & Hall/CRC Mathematical & Computational Biology
- Series Part/Volume Number44
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note84 black & white illustrations, 2 black & white tables
- Weight657 g
- Width156 mm
- Height235 mm
- Spine23 mm
- Format DetailsUnsewn / adhesive bound
- Edition Statement2nd Revised edition
- Table Of ContentsModelling 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.
- Author BiographyDarren 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.
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