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  3. Biosimulation
  • Cited by 10
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2012
Online ISBN:
9781139045582
DOI:
https://doi.org/10.1017/CBO9781139045582
Subjects:
Physics and Astronomy, Engineering, Biological Physics and Soft Matter Physics, Biomedical Engineering
Series:
Cambridge Texts in Biomedical Engineering
Information

Book description

This practical guide to biosimulation provides the hands-on experience needed to devise, design and analyze simulations of biophysical processes for applications in biological and biomedical sciences. Through real-world case studies and worked examples, students will develop and apply basic operations through to advanced concepts, covering a wide range of biophysical topics including chemical kinetics and thermodynamics, transport phenomena, and cellular electrophysiology. Each chapter is built around case studies in a given application area, with simulations of real biological systems developed to analyze and interpret data. Open-ended project-based exercises are provided at the end of each chapter, and with all data and computer codes available online (www.cambridge.org/biosim) students can quickly and easily run, manipulate, explore and expand on the examples inside. This hands-on guide is ideal for use on senior undergraduate/graduate courses and also as a self-study guide for anyone who needs to develop computational models of biological systems.

Reviews

"concise, very readable textbook..overall, this is a nicely written text that focuses on the more practical aspect of biosimulation, and avoids bogging down the reader with esoteric mathematical theorems and proofs. Recommended." - M.R. King, CHOICE, December 2012

"...this publication does justice to its central aim of providing practical guidance in the development of mathematical models of biological systems. It would likely serve well as a textbook for a course addressing the main subject areas, and as a reference for researchers working in those areas who wish to build such models or better understand their construction." - Christopher R. Myers, Cornell University, Quarterly Review of Biology, June 2013

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