I. Modelling and networks. Introduction to biological modelling -- Representation of biochemical networks -- II. Stochastic processes and simulation. Probability models -- Stochastic simulation -- Markov processes -- III. Stochastic chemical kinetics. Chemical and biochemical kinetics -- Case studies -- Beyond the Gillespie algorithm -- IV. Bayesian inference. Bayesian inference and MCMC -- Inference for stochastic kinetic models -- Conclusions
Summary
"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"--Provided by publisher
