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Stochastic Modelling for Systems Biology, Third Edition 3rd edition [Pehme köide]

(Newcastle University, UK)
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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.

Arvustused

"...stochastic modeling has drawn the attention of many researchers in biology and physiology. A textbook, with much elaboration, is highly valuable to understanding the underlying mathematical and computational methods in biological stochastic modeling. Prof Wilkinson has designed the content of this book to fill a gap in the educational text/reference books available for students/researchers learning about stochastic modeling in biological systems... This third edition book almost covers all of the material necessary for students studying stochastic kinetics modelling. The exercises in every chapter certainly illustrate the theory and concept of the book. Appendices A and B elaborate on all of the SBML code and other software associated with the book. The codes are also complemented by links to the authors webpage and a GitHub repository. The author must be appreciated for adding so many references for further reading. The content of the book is designed for a one-semester graduate-level course in stochastic modeling in biology. Thus, this book is targeted at master and graduate students in interdisciplinary subjects such as applied mathematics, computational biology, bioinformatics, biophysics, Biochemistry, and biomedical engineering." - Chitaranjan Mahapatra, Appeared in ISCB News, January 2020

Author ix
Acknowledgments xi
Preface to the third edition xiii
Preface to the second edition xv
Preface to the first edition xvii
I Modelling and networks
1(50)
1 Introduction to biological modelling
3(20)
1.1 What is modelling?
3(1)
1.2 Aims of modelling
4(1)
1.3 Why is stochastic modelling necessary?
4(4)
1.4 Chemical reactions
8(2)
1.5 Modelling genetic and biochemical networks
10(8)
1.6 Modelling higher-level systems
18(2)
1.7 Exercises
20(1)
1.8 Further reading
20(3)
2 Representation of biochemical networks
23(28)
2.1 Coupled chemical reactions
23(1)
2.2 Graphical representations
24(2)
2.3 Petri nets
26(10)
2.4 Stochastic process algebras
36(2)
2.5 Systems Biology Markup Language (SBML)
38(5)
2.6 SBML-shorthand
43(6)
2.7 Exercises
49(1)
2.8 Further reading
50(1)
II Stochastic processes and simulation
51(120)
3 Probability models
53(48)
3.1 Probability
53(11)
3.2 Discrete probability models
64(8)
3.3 The discrete uniform distribution
72(1)
3.4 The binomial distribution
73(1)
3.5 The geometric distribution
73(3)
3.6 The Poisson distribution
76(3)
3.7 Continuous probability models
79(5)
3.8 The uniform distribution
84(3)
3.9 The exponential distribution
87(4)
3.10 The normal/Gaussian distribution
91(4)
3.11 The gamma distribution
95(3)
3.12 Quantifying `noise'
98(1)
3.13 Exercises
99(1)
3.14 Further reading
100(1)
4 Stochastic simulation
101(24)
4.1 Introduction
101(1)
4.2 Monte Carlo integration
101(1)
4.3 Uniform random number generation
102(1)
4.4 Transformation methods
103(5)
4.5 Lookup methods
108(1)
4.6 Rejection samplers
109(3)
4.7 Importance resampling
112(1)
4.8 The Poisson process
113(1)
4.9 Using the statistical programming language R
114(6)
4.10 Analysis of simulation output
120(2)
4.11 Exercises
122(2)
4.12 Further reading
124(1)
5 Markov processes
125(46)
5.1 Introduction
125(1)
5.2 Finite discrete time Markov chains
125(7)
5.3 Markov chains with continuous state-space
132(7)
5.4 Markov chains in continuous time
139(15)
5.5 Diffusion processes
154(14)
5.6 Exercises
168(2)
5.7 Further reading
170(1)
III Stochastic chemical kinetics
171(100)
6 Chemical and biochemical kinetics
173(32)
6.1 Classical continuous deterministic chemical kinetics
173(7)
6.2 Molecular approach to kinetics
180(2)
6.3 Mass-action stochastic kinetics
182(2)
6.4 The Gillespie algorithm
184(1)
6.5 Stochastic Petri nets (SPNs)
185(3)
6.6 Structuring stochastic simulation codes
188(3)
6.7 Rate constant conversion
191(5)
6.8 Kolmogorov's equations and other analytic representations
196(5)
6.9 Software for simulating stochastic kinetic networks
201(1)
6.10 Exercises
202(1)
6.11 Further reading
203(2)
7 Case studies
205(18)
7.1 Introduction
205(1)
7.2 Dimerisation kinetics
205(5)
7.3 Michaelis-Menten enzyme kinetics
210(4)
7.4 An auto-regulatory genetic network
214(5)
7.5 The lac operon
219(2)
7.6 Exercises
221(1)
7.7 Further reading
222(1)
8 Beyond the Gillespie algorithm
223(26)
8.1 Introduction
223(1)
8.2 Exact simulation methods
223(5)
8.3 Approximate simulation strategies
228(13)
8.4 Hybrid simulation strategies
241(6)
8.5 Exercises
247(1)
8.6 Further reading
247(2)
9 Spatially extended systems
249(22)
9.1 Introduction
249(1)
9.2 One-dimensional reaction-diffusion systems
250(8)
9.3 Two-dimensional reaction-diffusion systems
258(1)
9.4 Exercises
259(1)
9.5 Further reading
260(11)
IV Bayesian inference
271(84)
10 Bayesian inference and MCMC
273(30)
10.1 Likelihood and Bayesian inference
273(5)
10.2 The Gibbs sampler
278(10)
10.3 The Metropolis-Hastings algorithm
288(4)
10.4 Hybrid MCMC schemes
292(1)
10.5 Metropolis-Hastings algorithms for Bayesian inference
293(1)
10.6 Bayesian inference for latent variable models
294(5)
10.7 Alternatives to MCMC
299(2)
10.8 Exercises
301(1)
10.9 Further reading
302(1)
11 Inference for stochastic kinetic models
303(48)
11.1 Introduction
303(1)
11.2 Inference given complete data
304(3)
11.3 Discrete-time observations of the system state
307(7)
11.4 Diffusion approximations for inference
314(4)
11.5 Likelihood-free methods
318(17)
11.6 Approximate Bayesian computation (ABC) for parameter inference
335(9)
11.7 Network inference and model comparison
344(3)
11.8 Exercises
347(2)
11.9 Further reading
349(2)
12 Conclusions
351(4)
Apppendix A SBML Models
355(8)
A.1 Auto-regulatory network
355(3)
A.2 Lotka-Volterra reaction system
358(1)
A.3 Dimerisation-kinetics model
359(4)
Apppendix B Software associated with this book
363(4)
B.1 Smfsb R package
363(1)
B.2 SBML-shorthand
364(1)
B.3 Smfsbsbml R package
364(1)
B.4 scala-smfsb library
365(2)
Bibliography 367(12)
Index 379
Darren Wilkinson is Professor of Stochastic modelling at Newcastle University in the United Kingdom. He was educated at the nearby University of Durham, where he took his first degree in Mathematics followed by a PhD in Bayesian statistics which he completed in 1995. He moved to a Lectureship in Statistics at Newcastle University in 1996, where he has remained since, being promoted to his current post in 2007. Professor Wilkinson is interested in computational statistics and Bayesian inference and in the application of modern statistical technology to problems in statistical bioinformatics and systems biology. He is also interested in some of the big data challenges that arise in bioscience and more generally. He serves on Biotechnology and Biological Sciences Research Councils Strategy Advisory Panel for Exploiting new ways of working and is co-Director of Newcastles Engineering and Physical Sciences Research Council Centre for Doctoral Training in Cloud Computing for Big Data. He is also a Fellow of the Alan Turing Institute for data science and artificial intelligence.