Stochastic Approaches for Systems Biology

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 and Bioinformatics 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

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