| Description |
1 online resource (405 pages) |
| Series |
Chapman and Hall/CRC Mathematical and Computational Biology Ser |
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Chapman and Hall/CRC Mathematical and Computational Biology Ser
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| Contents |
Cover; Half Title; Title Page; Copyright Page; Table of Contents; Author; Acknowledgments; Preface to the third edition; Preface to the second edition; Preface to the first edition; I: Modelling and networks; 1: Introduction to biological modelling; 1.1 What is modelling?; 1.2 Aims of modelling; 1.3 Why is stochastic modelling necessary?; 1.4 Chemical reactions; 1.5 Modelling genetic and biochemical networks; 1.6 Modelling higher-level systems; 1.7 Exercises; 1.8 Further reading; 2: Representation of biochemical networks; 2.1 Coupled chemical reactions; 2.2 Graphical representations |
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2.3 Petri nets2.4 Stochastic process algebras; 2.5 Systems Biology Markup Language (SBML); 2.6 SBML-shorthand; 2.7 Exercises; 2.8 Further reading; II: Stochastic processes and simulation; 3: Probability models; 3.1 Probability; 3.2 Discrete probability models; 3.3 The discrete uniform distribution; 3.4 The binomial distribution; 3.5 The geometric distribution; 3.6 The Poisson distribution; 3.7 Continuous probability models; 3.8 The uniform distribution; 3.9 The exponential distribution; 3.10 The normal/Gaussian distribution; 3.11 The gamma distribution; 3.12 Quantifying 'noise' |
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3.13 Exercises3.14 Further reading; 4: Stochastic simulation; 4.1 Introduction; 4.2 Monte Carlo integration; 4.3 Uniform random number generation; 4.4 Transformation methods; 4.5 Lookup methods; 4.6 Rejection samplers; 4.7 Importance resampling; 4.8 The Poisson process; 4.9 Using the statistical programming language R; 4.10 Analysis of simulation output; 4.11 Exercises; 4.12 Further reading; 5: Markov processes; 5.1 Introduction; 5.2 Finite discrete time Markov chains; 5.3 Markov chains with continuous state-space; 5.4 Markov chains in continuous time; 5.5 Diffusion processes; 5.6 Exercises |
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5.7 Further readingIII: Stochastic chemical kinetics; 6: Chemical and biochemical kinetics; 6.1 Classical continuous deterministic chemical kinetics; 6.2 Molecular approach to kinetics; 6.3 Mass-action stochastic kinetics; 6.4 The Gillespie algorithm; 6.5 Stochastic Petri nets (SPNs); 6.6 Structuring stochastic simulation codes; 6.7 Rate constant conversion; 6.8 Kolmogorov's equations and other analytic representations; 6.9 Software for simulating stochastic kinetic networks; 6.10 Exercises; 6.11 Further reading; 7: Case studies; 7.1 Introduction; 7.2 Dimerisation kinetics |
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7.3 Michaelis-Menten enzyme kinetics7.4 An auto-regulatory genetic network; 7.5 The lac operon; 7.6 Exercises; 7.7 Further reading; 8: Beyond the Gillespie algorithm; 8.1 Introduction; 8.2 Exact simulation methods; 8.3 Approximate simulation strategies; 8.4 Hybrid simulation strategies; 8.5 Exercises; 8.6 Further reading; 9: Spatially extended systems; 9.1 Introduction; 9.2 One-dimensional reaction-diffusion systems; 9.3 Two-dimensional reaction-diffusion systems; 9.4 Exercises; 9.5 Further reading; IV: Bayesian inference; 10: Bayesian inference and MCMC |
| 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. 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) Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. 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 Keeping with the spirit of earlier editions, 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. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling |
| Notes |
10.1 Likelihood and Bayesian inference |
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Print version record |
| Subject |
Biological systems -- Mathematical models
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Systems biology.
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Systems Biology
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SCIENCE -- Life Sciences -- Biology -- General.
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SCIENCE -- Biotechnology.
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Bayesian.
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Bayesian Inference.
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Case Studies.
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kinetics.
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Modelling and Networks.
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networks.
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SBML.
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Scala.
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Stochastic Chemical Kinetics.
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Stochastic Processes and Simulation.
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stochastic processes.
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Biological systems -- Mathematical models
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Systems biology
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| Form |
Electronic book
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| ISBN |
9781351000901 |
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135100090X |
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1138549282 |
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9781138549289 |
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9781351000895 |
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1351000896 |
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