Stochastic Modelling for Systems Biology
1st Edition
Published on 18. April 2006
Book
Hardback
280 pages
978-1-58488-540-5 (ISBN)
Description
Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. The stochastic processes formalism provides a beautiful, elegant, and coherent foundation for chemical kinetics and there is a wealth of associated theory every bit as powerful and elegant as that for conventional continuous deterministic models. The time is right for an introductory text written from this perspective.
Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications.
While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source.
Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications.
While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source.
Reviews / Votes
"This book is an excellent introduction to the concepts of stochastic modelling relevant for system biology applications based on stochastic processes . . . strongly recommended for classroom use, especially for computational systems biologists and statisticians."- W. Urfer, in Statistical Papers, 2007, Vol. 48
More details
Series
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Graduate students and researchers in bioinformatics, systems biology, statistics, and mathematics
Illustrations
80 s/w Abbildungen
80 Illustrations, black and white
Dimensions
Height: 235 mm
Width: 159 mm
Weight
522 gr
ISBN-13
978-1-58488-540-5 (9781584885405)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Darren J. Wilkinson
Stochastic Modelling for Systems Biology
Book
11/2011
2nd Edition
CRC Press
€92.99
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Content
INTRODUCTION TO BIOLOGICAL MODELLING
What is Modelling?
Aims of Modelling
Why is Stochastic Modelling Necessary?
Chemical Reactions
Modelling Genetic and Biochemical Networks
Modelling Higher-Level Systems
Exercises
Further Reading
REPRESENTATION OF BIOCHEMICAL NETWORKS
Coupled Chemical Reactions
Graphical Representations
Petri Nets
Systems Biology Markup Language (SBML)
SBML-Shorthand
Exercises
Further Reading
PROBABILITY MODELS
Probability
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
Exercises
Further reading
STOCHASTIC SIMULATION
Introduction
Monte-Carlo Integration
Uniform Random Number Generation
Transformation Methods
Lookup Methods
Rejection Samplers
The Poisson Process
Using the Statistical Programming Language, R
Analysis of Simulation Output
Exercises
Further Reading
MARKOV PROCESSES
Introduction
Finite Discrete Time Markov Chains
Markov Chains with Continuous State Space
Markov Chains in Continuous Time
Diffusion Processes
Exercises
Further reading
CHEMICAL AND BIOCHEMICAL KINETICS
Classical Continuous Deterministic Chemical Kinetics
Molecular Approach to Kinetics
Mass-Action Stochastic Kinetics
The Gillespie Algorithm
Stochastic Petri Nets (SPNs)
Rate Constant Conversion
The Master Equation
Software for Simulating Stochastic Kinetic Networks
Exercises
Further Reading
CASE STUDIES
Introduction
Dimerisation Kinetics
Michaelis-Menten Enzyme Kinetics
An Auto-Regulatory Genetic Network
The Lac operon
Exercises
Further Reading
BEYOND THE GILLESPIE ALGORITHM
Introduction
Exact Simulation Methods
Approximate Simulation Strategies
Hybrid Simulation Strategies
Exercises
Further reading
BAYESIAN INFERENCE AND MCMC
Likelihood and Bayesian Inference
The Gibbs Sampler
The Metropolis-Hastings Algorithm
Hybrid MCMC Schemes
Exercises
Further reading
INFERENCE FOR STOCHASTIC KINETIC MODELS
Introduction
Inference Given Complete Data
Discrete-Time Observations of the System State
Diffusion Approximations for Inference
Network Inference
Exercises
Further reading
CONCLUSIONS
A SBML Models
A.1 Auto-Regulatory Network
A.2 Lotka-Volterra Reaction System
A.3 Dimerisation-Kinetics Model
References
Index
What is Modelling?
Aims of Modelling
Why is Stochastic Modelling Necessary?
Chemical Reactions
Modelling Genetic and Biochemical Networks
Modelling Higher-Level Systems
Exercises
Further Reading
REPRESENTATION OF BIOCHEMICAL NETWORKS
Coupled Chemical Reactions
Graphical Representations
Petri Nets
Systems Biology Markup Language (SBML)
SBML-Shorthand
Exercises
Further Reading
PROBABILITY MODELS
Probability
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
Exercises
Further reading
STOCHASTIC SIMULATION
Introduction
Monte-Carlo Integration
Uniform Random Number Generation
Transformation Methods
Lookup Methods
Rejection Samplers
The Poisson Process
Using the Statistical Programming Language, R
Analysis of Simulation Output
Exercises
Further Reading
MARKOV PROCESSES
Introduction
Finite Discrete Time Markov Chains
Markov Chains with Continuous State Space
Markov Chains in Continuous Time
Diffusion Processes
Exercises
Further reading
CHEMICAL AND BIOCHEMICAL KINETICS
Classical Continuous Deterministic Chemical Kinetics
Molecular Approach to Kinetics
Mass-Action Stochastic Kinetics
The Gillespie Algorithm
Stochastic Petri Nets (SPNs)
Rate Constant Conversion
The Master Equation
Software for Simulating Stochastic Kinetic Networks
Exercises
Further Reading
CASE STUDIES
Introduction
Dimerisation Kinetics
Michaelis-Menten Enzyme Kinetics
An Auto-Regulatory Genetic Network
The Lac operon
Exercises
Further Reading
BEYOND THE GILLESPIE ALGORITHM
Introduction
Exact Simulation Methods
Approximate Simulation Strategies
Hybrid Simulation Strategies
Exercises
Further reading
BAYESIAN INFERENCE AND MCMC
Likelihood and Bayesian Inference
The Gibbs Sampler
The Metropolis-Hastings Algorithm
Hybrid MCMC Schemes
Exercises
Further reading
INFERENCE FOR STOCHASTIC KINETIC MODELS
Introduction
Inference Given Complete Data
Discrete-Time Observations of the System State
Diffusion Approximations for Inference
Network Inference
Exercises
Further reading
CONCLUSIONS
A SBML Models
A.1 Auto-Regulatory Network
A.2 Lotka-Volterra Reaction System
A.3 Dimerisation-Kinetics Model
References
Index