Stochastic Approaches for Systems Biology

This book presents key concepts in stochastic approaches for systems biology. It focuses on both analytical and numerical approaches and features Matlab examples as well as many illustrations.

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.

Focuses on both analytical and numerical approaches Key Concepts in Stochastic Processes Matlab examples provided Many illustrations and examples included Includes supplementary material: sn.pub/extras

Autorentext

Olaf Wolkenhauer received his first degrees in control engineering from the University of Applied Sciences in Hamburg, Germany and the University of Portsmouth in the U.K. He obtained his PhD from the University of Manchester, Institute of Science & Technology (UMIST) for research on the application of possibility theory to data analysis. Following a research lectureship at the Control Systems Centre at UMIST and a research fellowship at the Technical University Delft in the Netherlands, he directed his research towards systems biology. This led to a joint senior lectureship with the Dept. of Biomolecular Sciences and the Dept. of Electrical Engineering and Electronics, at UMIST. In 2003 he moved to the University of Rostock in Germany, where he heads the Department of Systems Biology and Bioinformatics. He is a fellow of the Stellenbosch Institute for Advanced Study (STIAS). Olaf Wolkenhauer's research interests are in mathematical modelling and data analysis. His work concentrates on a systems-theoretic approach to understand the regulation, control and coordination of complex dynamical systems.

Webpage: www.sbi.uni-rostock.de

Mukhtar Ullah received his BSc in Electrical Engineering from N-W.F.P University of Engineering and Technology Peshawar, N-W.F.P, Pakistan. He completed his MSc. in Advanced Control and Systems Engineering from the Department of Electrical Engineering and Electronics, at the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K. in 2002. In 2003 he moved to the University of Rostock in Germany as a research assistant in the Department of Systems Biology andBioinformatics in the Institute of Computer Science. In 2009 he completed his PhD from the same department. He is currently working as a post-doctoral research associate in the same department. His research focuses on stochastic modelling of biological systems.

Webpage: www.sbi.uni-rostock.de

Klappentext
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.

Inhalt
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