Ulteriori informazioni
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.
Sommario
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
Info autore
Olaf Wolkenhauer wurde 1966 in Buchholz in der Nordheide geboren, absolvierte eine Ausbildung bei der AEG und studierte bis 1994 in Hamburg und Portsmouth. 1997 promovierte er am Institut für Wissenschaft und Technologie (UMIST) in Manchester, England. Dort arbeitete er nach der Promotion als Dozent bis er 2003 an die Universität Rostock berufen wurde, wo er den Lehrstuhl für Systembiologie und Bioinformatik leitet.
Riassunto
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.
