Stochastic Modelling for Systems Biology: (Chapman & Hall/CRC Mathematical and Computational Biology)

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 which will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Table of Contents:
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.

About the Author :
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.

Review :
Praise for the First Edition !designed and well suited as an in-depth introduction into stochastic chemical simulation, both for self-study or as a course text! --Biomedical Engineering Online, December 2006