Stochastic modeling for systems biology/ Darren J Wlkinson

Includes bibliography and index

Modelling and networks -- Stochastic processes and simulation -- Stochastic chemical kinetics -- Bayesian inference

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. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC).
New in the Third Edition

New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation
Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC
Updated R package, including code relating to all of the new material
New R package for parsing SBML models into simulatable stochastic Petri net models
New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language.