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This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies.
This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.
List of contents
Introduction.- Deterministic Simulation Algorithms.- Stochastic Simulation Algorithms.- Hybrid Simulation Algorithms.- Reaction-Diffusion Systems.- Conclusions and Perspectives.
About the author
Luca Marchetti is the head of the computational biology team at COSBI, the Centre for Computational and Systems Biology, a bioinformatics company jointly owned by Microsoft Research and the University of Trento. He is also Contract Professor at the University of Verona, and an Associate Editor of the journal Optimization, Frontiers in Applied Mathematics and Statistics. He is in charge of several research projects in collaboration with important universities and pharmaceutical companies, and he is the author of scientific papers in international journals, books and conference proceedings.
Corrado Priami has been a professor of computer science at the University of Trento since 2001. The results of his PhD thesis on stochastic pi-calculus were the basis for the foundation of COSBI. He has published over 190 scientific papers, given more than 90 invited talks and lectures, and regularly serves in related advisory, scientific, and reviewing boards. He is a visiting professor atStanford University.
Vo Hong Thanh is a researcher at COSBI. His research interests include computational biology, chemical physics, and computational physics.
Summary
This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies.
This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.
Report
"I will not hesitate to recommend ... this book, both as an introductory explanation as well as later on when they are deep in a modeling exercise and need to understand the many subtle yet important variations of stochastic simulation techniques applicable to biological systems." (Sara Kalvala, Computing Reviews, March, 2018)
