Stochastic Modelling for Systems Biology available in Hardcover
- Pub. Date:
- Taylor & Francis
Provides a self-contained introduction to the stochastic modelling of biological and genetic networks Focuses on computer simulation with methods illustrated by concrete example implementations in R Includes an introduction to Bayesian inference for parameter estimation in stochastic kinetic models Covers the latest fast approximate and hybrid simulation techniques Includes example models encoded in SBML and available for downloading from the Web 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.
|Publisher:||Taylor & Francis|
|Series:||Chapman & Hall/CRC Mathematical and Computational Biology Series|
|Edition description:||Older Edition|
|Product dimensions:||6.20(w) x 9.30(h) x 0.70(d)|
Table of Contents
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
A SBML Models A.1 Auto-Regulatory Network A.2 Lotka-Volterra Reaction System A.3 Dimerisation-Kinetics Model