Guide to Simulation and Modeling for Biosciences

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, stochastic modeling techniques, differential equations, spatial simulations, and Gillespie's stochastic 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.

Presents a broad overview of the most important techniques used to model biological systems Provides a detailed introduction to agent-based models, stochastic modeling techniques, and spatial simulations for the novice modeler With exercises, and a companion website featuring downloadable sample code Includes supplementary material: sn.pub/extras

Autorentext

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.

Klappentext

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.

  Inhalt

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