Deterministic versus stochastic modelling in biochemistry and systems biology
著者
書誌事項
Deterministic versus stochastic modelling in biochemistry and systems biology
（Woodhead Publishing series in biomedicine, 21）
Woodhead Pub., 2013
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics.
目次
List of figures
List of tables
Preface
About the Authors and Contributors
Chapter 1: Deterministic chemical kinetics
Abstract:
1.1 Determinism and Chemistry
1.2 The Material Balance
1.3 The Rate Law
1.4 Solving the Conservation Equations
1.5 Simple Reaction Mechanisms
1.6 The Law of Mass Action
1.7 Conclusions
Chapter 2: The stochastic approach to biochemical kinetics
Abstract:
2.1 Introduction
2.2 The chemical master equation
2.3 Solution of the Master Equation
The irreversible reaction A → B
The Irreversible Reaction A + B → C
Other Irreversible Bimolecular Reactions
The reversible reaction A + B C at equilibrium
Other reversible bimolecular reactions at equilibrium
2.4 The relationship between the deterministic and stochastic formalisms
Chapter 3: The exact stochastic simulation algorithms
Abstract.
3.1 Introduction
3.2 The reaction probability density function
3.3 The stochastic simulation algorithms
3.4 Case studies
3.5 Caveats regarding the modeling of living systems
Chapter 4: Modelling in systems biology
Abstract
4.1 What is biological modeling
4.2 System Biology
4.3 Complexity of a biological system
4.4 Stochastic modeling approach
4.5 Formalizing complexity
Chapter 5: The structure of biochemical models
Abstract
5.1 Classification of biological processes and mathematical formalism
5.2 Spatially Homogeneous Models
5.3 Variants of the SSA for non-Markovian and non-homogeneous processes
Chapter 6: Reaction-diffusion systems
Abstract
6.1 Introduction
6.2 A generalization of the Fick’s law
6.3 The optimal size of the system’s subvolumes
6.4 The algorithm and data structure
6.5 Case study 1: chaperone-assisted folding
6.6 Case study 2: modeling the formation of Bicoid gradient
6.7 Conclusions and future directions
Chapter 7: KInfer: a tool for model calibration
Abstract
7.1 Introduction
7.2 The model for inference
7.3 Synthetic case study: buffering SERCA pump
7.4 Real case studies
7.5 Glucose metabolisms of Lactococcus lactis
7.6 Discussion
Chapter 8: Modelling living systems with BlenX
Abstract
8.1 Deterministic vs stochastic approach in systems biology
8.2 The BlenX language
8.3 The ubiquitin-proteasome system
8.4 A predator-prey model
8.5 Conclusions
Chapter 9: Simulation of ecodynamics: key nodes in food webs
Abstract
9.1 Systems ecology
9.2 Ecological interaction networks
9.3 Pattern and process
9.4 Food web dynamics: simulation and sensitivity analysis
Notes
Index
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