Guide to Simulation and Modeling for Biosciences

by David J. Barnes, Dominique Chu

Overview

This accessible text presents a detailed introduction to the use of a wide range of software tools and modeling environments for use in the biosciences, as well as the fundamental mathematical background. The practical constraints presented by each modeling technique are described in detail, enabling the researcher to determine which software package would be most useful for a particular problem. Features: introduces a basic array of techniques to formulate models of biological systems, and to solve them; discusses agent-based models, shastic modeling techniques, differential equations, spatial simulations, and Gillespie’s shastic simulation algorithm; provides exercises; describes such useful tools as the Maxima algebra system, the PRISM model checker, and the modeling environments Repast Simphony and Smoldyn; contains appendices on rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts; offers supplementary material at an associated website.

Product Details

ISBN-13:	9781447167617
Publisher:	Springer London
Publication date:	09/03/2015
Series:	Simulation Foundations, Methods and Applications
Edition description:	2nd ed. 2015
Pages:	339
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

About the Author

David J. Barnes is a senior lecturer in computer science at the University of Kent, UK, with a strong background in the teaching of programming and the implementation of computational models of biological systems.

Dominique Chu is a senior lecturer in computer science at the University of Kent, UK. He is an expert in mathematical and computational modeling of biological systems, with years of experience in these fields.

Table of Contents

Foundations of Modeling.- Agent-based Modeling.- ABMs using Repast Simphony.- Differential Equations.- Mathematical Tools.- Other Shastic Methods and Prism.- Simulating Biochemical Systems.- Biochemical Models Beyond the Perfect Mixing Assumption.- Reference Material.