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.

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.