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Stochastic Modelling for Systems Biology, Third Edition
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 everythi...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Milton :
Chapman and Hall/CRC,
2018.
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| Edición: | 3rd ed. |
| Colección: | Chapman and Hall/CRC Mathematical and Computational Biology Ser.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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'
- 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
- 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
- 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