Read more
Informationen zum Autor Paola Lecca is a researcher at The Microsoft Research, University of Trento, Centre for Computational and Systems Biology, Italy. Her principal research interests include biochemical system identification and model calibration, stochastic chemical kinetics and reaction-diffusion system. Ian J. Laurenzi is a Senior Researcher at ExxonMobil. He is an expert in the areas of stochastic processes, statistics, computational biology and receptor-mediated adhesion of human blood platelets, and has investigated the genome-level dynamics of gene networks and the effect of sex upon mammalian gene expression. Klappentext Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and the stochastic foundations of biochemical kinetics. Zusammenfassung Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and the stochastic foundations of biochemical kinetics. Inhaltsverzeichnis Deterministic chemical kinetics; The stochastic approach to biochemical kinetics; The exact stochastic simulation algorithms; Modelling in systems biology; The structure of biochemical models; Reaction-diffusion systems; KInfer: A tool for model calibration; Modelling living systems with BlenX; Simulation of ecodynamics: Key nodes in food webs.
List of contents
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
