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Stochastic Dynamics for Systems Biology

Christian Mazza | Michel Benaim
€91.99
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A01=Christian Mazza
A01=Michel Benaim
advanced stochastic modeling for cellular networks
Author_Christian Mazza
Author_Michel Benaim
Binding Sites
biochemical pathway analysis
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Chemical Reaction Networks
Detailed Balance Equation
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Free Energy Function
gene expression regulation
Gene Networks
Gibbs Boltzmann Distribution
Hill Coefficient
Hill Sense
Invariant Measure
Invariant Probability Measure
Ligand Molecule
Markov Chain
Markov process applications
Mass Action
Mass Action Kinetics
Mass Action Principle
Matrix Tree Theorem
Nucleosomal DNA
Omega Limit Set
Ordinary Differential Equation
Phase Portrait
population dynamics models
Propensity Functions
statistical mechanics in biology
Steady State Distribution
systems biology modeling
Time Continuous Markov Chains
Unique Invariant Probability Measure
Vice Versa

Product details

  • ISBN 9781466514935
  • Weight: 600g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 Mar 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

Stochastic Dynamics for Systems Biology is one of the first books to provide a systematic study of the many stochastic models used in systems biology. The book shows how the mathematical models are used as technical tools for simulating biological processes and how the models lead to conceptual insights on the functioning of the cellular processing system. Most of the text should be accessible to scientists with basic knowledge in calculus and probability theory.

The authors illustrate the relevant Markov chain theory using realistic models from systems biology, including signaling and metabolic pathways, phosphorylation processes, genetic switches, and transcription. A central part of the book presents an original and up-to-date treatment of cooperativity. The book defines classical indexes, such as the Hill coefficient, using notions from statistical mechanics. It explains why binding curves often have S-shapes and why cooperative behaviors can lead to ultrasensitive genetic switches. These notions are then used to model transcription rates. Examples cover the phage lambda genetic switch and eukaryotic gene expression.

The book then presents a short course on dynamical systems and describes stochastic aspects of linear noise approximation. This mathematical framework enables the simplification of complex stochastic dynamics using Gaussian processes and nonlinear ODEs. Simple examples illustrate the technique in noise propagation in gene networks and the effects of network structures on multistability and gene expression noise levels. The last chapter provides up-to-date results on stochastic and deterministic mass action kinetics with applications to enzymatic biochemical reactions and metabolic pathways.

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