Guide to Simulation and Modeling for Biosciences

  • David J. Barnes
  • Dominique Chu

Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Table of contents

  1. Front Matter
    Pages i-xii
  2. David J. Barnes, Dominique Chu
    Pages 1-14
  3. David J. Barnes, Dominique Chu
    Pages 15-78
  4. David J. Barnes, Dominique Chu
    Pages 79-119
  5. David J. Barnes, Dominique Chu
    Pages 121-174
  6. David J. Barnes, Dominique Chu
    Pages 175-206
  7. David J. Barnes, Dominique Chu
    Pages 207-264
  8. David J. Barnes, Dominique Chu
    Pages 265-299
  9. David J. Barnes, Dominique Chu
    Pages 301-324
  10. Back Matter
    Pages 325-339

About this book

Introduction

This accessible text/reference 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 some of the fundamental mathematical background. The practical constraints and difficulties presented by each modeling technique are described in detail, enabling the researcher to determine quickly which software package would be most useful for their particular problem.

This Guide to Simulation and Modeling for Biosciences is a fully updated and enhanced revision of the authors’ earlier Introduction to Modeling for Biosciences. Written with the particular needs of the novice modeler in mind, this unique and helpful work guides the reader through realistic and concrete modeling projects, highlighting and commenting on the process of abstracting the real system into a model.

Topics and features:

  • Introduces a basic array of techniques to formulate models of biological systems, and to solve them
  • Discusses agent-based models, stochastic modeling techniques, differential equations, spatial simulations, and Gillespie’s stochastic simulation algorithm
  • Provides exercises to help the reader sharpen their understanding of the topics
  • 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,
including source code for many of the example models discussed in the book

Students and active researchers in the biosciences will benefit from the discussions of the high-quality, tried-and-tested modeling tools described in the book, as well as the thorough descriptions and examples.

Keywords

Agent-Based Modeling Algorithms Bio-Modeling Computational Biology Computer Algebra Differential Equation Models Linear Optimization Markov Chains Model

Authors and affiliations

  • David J. Barnes
    • 1
  • Dominique Chu
    • 2
  1. 1.University of KentCanterburyUnited Kingdom
  2. 2.School of ComputingUniversity of KentCanterburyUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-6762-4
  • Copyright Information Springer-Verlag London 2015
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-4471-6761-7
  • Online ISBN 978-1-4471-6762-4
  • Series Print ISSN 2195-2817
  • Series Online ISSN 2195-2825
  • About this book