Edition:

2

Publisher:

Chapman & Hall/CRC

Number of Pages:

335

Price:

89.95

ISBN:

9781439837726

Date Received:

Thursday, November 17, 2011

Reviewable:

Yes

Reviewer Email Address:

Series:

Chapman & Hall/CRC Mathematical and Computational Biology Series

Publication Date:

2012

Format:

Hardcover

Audience:

Category:

Textbook

*All Chapters include Exercises and Further Reading*

Modelling and networks

Introduction to biological modelling

Aims of modelling

Why is stochastic modelling necessary?

Chemical reactions

Modelling genetic and biochemical networks

Modelling higher-level systems

Graphical representations

Petri nets

Stochastic process algebras

Systems Biology Markup Language (SBML)

SBML-shorthand

Probability models

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"

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

Finite discrete time Markov chains

Markov chains with continuous state-space

Markov chains in continuous time

Diffusion processes

Chemical and biochemical 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

Dimerisation kinetics

Michaelis–Menten enzyme kinetics

An auto-regulatory genetic network

The

Exact simulation methods

Approximate simulation strategies

Hybrid simulation strategies

Bayesian inference and MCMC

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 given complete data

Discrete-time observations of the system state

Diffusion approximations for inference

Likelihood-free methods

Network inference and model comparison

SBML Models

Lotka–Volterra reaction system

Dimerisation-kinetics model

References

Index

Publish Book:

Modify Date:

Thursday, November 17, 2011

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