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Taylor & Francis
Stochastic Modelling for Systems Biology

Stochastic Modelling for Systems Biology


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Product Details

ISBN-13: 9781584885405
Publisher: Taylor & Francis
Publication date: 06/15/2006
Series: Chapman & Hall/CRC Mathematical and Computational Biology Series
Edition description: Older Edition
Pages: 280
Product dimensions: 6.20(w) x 9.30(h) x 0.70(d)

Table of Contents

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

References Index

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