Introduction to Modeling for Biosciences

  • David J. Barnes
  • Dominique Chu

Table of contents

  1. Front Matter
    Pages I-XII
  2. David J. Barnes, Dominique Chu
    Pages 1-13
  3. David J. Barnes, Dominique Chu
    Pages 15-77
  4. David J. Barnes, Dominique Chu
    Pages 79-130
  5. David J. Barnes, Dominique Chu
    Pages 131-182
  6. David J. Barnes, Dominique Chu
    Pages 183-214
  7. David J. Barnes, Dominique Chu
    Pages 215-272
  8. David J. Barnes, Dominique Chu
    Pages 273-305
  9. Back Matter
    Pages 307-322

About this book

Introduction

Computational modeling has become an essential tool for researchers in the biological sciences. Yet in biological modeling, there is no one technique that is suitable for all problems. Instead, different problems call for different approaches. Furthermore, it can be helpful to analyze the same system using a variety of approaches, to be able to exploit the advantages and drawbacks of each. In practice, it is often unclear which modeling approaches will be most suitable for a particular biological question - a problem that requires researchers to know a reasonable amount about a number of techniques, rather than become experts on a single one.

Introduction to Modeling for Biosciences addresses this issue by presenting a broad overview of the most important techniques used to model biological systems. In addition to providing an introduction into the use of a wide range of software tools and modeling environments, this helpful text/reference describes the constraints and difficulties that each modeling technique presents in practice. This enables the researcher to quickly determine which software package would be most useful for their particular problem.

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 and Gillespie’s stochastic simulation algorithm
  • Intersperses the text with exercises
  • Includes practical introductions to the Maxima computer algebra system, the PRISM model checker, and the Repast Simphony agent modeling environment
  • Contains appendices on Repast batch running, rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts
  • Supplies source code for many of the example models discussed, at the associated website http://www.cs.kent.ac.uk/imb/

This unique and practical work guides the novice modeler through realistic and concrete modeling projects, highlighting and commenting on the process of abstracting the real system into a model. Students and active researchers in the biosciences will also benefit from the discussions of the high-quality, tried-and-tested modeling tools described in the book, as well as thorough descriptions and examples.

David J. Barnes is a lecturer in computer science at the University of Kent, UK, with a strong background in the teaching of programming.

Dominique Chu is a 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 subject fields.

Keywords

Agent-Based Modeling Agent-based Modelling Bio-modelling Computational Biology Differential Equation Models Markov Chains Probabilistic Modelling Methods Stochastic Mod algorithms computer algebra linear optimization model modeling simulation

Authors and affiliations

  • David J. Barnes
    • 1
  • Dominique Chu
    • 2
  1. 1.Computing LaboratoryUniversity of KentCanterburyUnited Kingdom
  2. 2.Computing LaboratoryUniversity of KentCanterburyUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84996-326-8
  • Copyright Information Springer-Verlag London Limited 2010
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-84996-325-1
  • Online ISBN 978-1-84996-326-8
  • About this book