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基本説明
This textbook focuses on stochastic modelling and its applications in systems biology. In addition to a review of probability theory, the authors introduce key concepts, including those of stochastic process, Markov property, and transition probability, side by side with notions of biochemical reaction networks. This leads to an intuitive presentation guided by a series of biological examples that are revisited throughout the text. The text shows how the notion of propensity, the chemical master equation and the stochastic simulation algorithm arise as consequences of the Markov property. The nontrivial relationships between various stochastic approaches are derived and illustrated. The text contains many illustrations, examples and exercises to communicate methods and analyses. Matlab code to simulate cellular systems is also provided where appropriate and the reader is encouraged to experiment with the examples and case studies provided. Senior undergraduate and graduate students in applied mathematics, the engineering and physical sciences as well as researchers working in the areas of systems biology, theoretical and computational biology will find this text useful.
Full Description
This textbook focuses on stochastic analysis in systems biology containing both the theory and application. While the authors provide a review of probability and random variables, subsequent notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of stochastic framework for modeling subcellular biochemical systems. In particular, the authors make an effort to show how the notion of propensity, the chemical master equation and the stochastic simulation algorithm arise as consequences of the Markov property.
The text contains many illustrations, examples and exercises to illustrate the ideas and methods that are introduced. Matlab code is also provided where appropriate. Additionally, the cell cycle is introduced as a more complex case study.
Senior undergraduate and graduate students in mathematics and physics as well as researchers working in the area of systems biology, bioinformatics and related areas will find this text useful.
Contents
Preface.- Acknowledgements.- Acronyms, notation.- Matlab functions, revisited examples.- Introduction.- Biochemical reaction networks.- Randomness.- Probability and random variables.- Stochastic modeling of biochemical networks.- The 2MA approach.- The 2MA cell cycle model.- Hybrid Markov processes.- Wet-lab experiments and noise.- Glossary
