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Computer Science - Theoretical Computer Science | Introduction to Modeling for Biosciences

Introduction to Modeling for Biosciences

Barnes, David J., Chu, Dominique

2010, XII, 322 p.

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  • Presents a broad overview of the most important techniques used to model biological systems
  • Provides a detailed introduction to agent-based models, stochastic modelling techniques and differential equations for the novice modeler
  • With exercises, and a companion website featuring software and sample programs, as well as downloadable sample code

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.

Content Level » Research

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

Related subjects » Bioinformatics - Mathematics - Systems Biology and Bioinformatics - Theoretical Computer Science

Table of contents 

Foundations of Modeling Agent-based modeling ABMs using Repast and Java Differential Equations Other Stochastic Methods and Prism Simulating Biochemical Systems Modeling: General Strategies and Ockham’s Razor

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