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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Foundations of Modeling

Abstract
This introductory chapter discusses some basic principles of biological modeling. Specifically, it presents some practical advice on how to approach modeling problems in biology. This chapter sets the scene for the rest of the book, and is particularly intended for the scientist who has little previous experience in modeling.
David J. Barnes, Dominique Chu

Chapter 2. Agent-Based Modeling

Abstract
This chapter introduces agent-based models (ABMs). These are computational semi-realistic models where every important part of the system is explicitly represented. ABMs can be very valuable in biological modeling because they can represent very complicated systems that cannot be represented using, for example, purely equation-based modeling approaches. This chapter explains the underlying ideas of ABMs, and highlights the characteristics that make systems amenable to ABM modeling. A large part comprises walk through illustrations of two models, namely, the spread of malaria in a spatially structured population and a model of the evolution of fimbriae. This last example also demonstrates how ABMs can be used to simulate evolution in a biologically realistic way.
David J. Barnes, Dominique Chu

Chapter 3. ABMs Using Repast Simphony

Abstract
This chapter provides the reader with a practical introduction to agent-based modeling via the Repast Simphony Agent Based Modeling toolkit. Using examples of agent-based models from an earlier chapter, we look in detail at how to build models using the Groovy programming language, which is based on Java. We illustrate some of the ways in which a toolkit such as Repast considerably simplifies the life of the modeler by providing extensive support for agent creation, model visualization, charting of results, and multiple runs.
David J. Barnes, Dominique Chu

Chapter 4. Differential Equations

Abstract
This chapter introduces the reader to the basic ideas underlying ordinary differential equations. Knowledge of the basic rules of differentiation and integration is assumed; however, one of the main objectives of the chapter is to convey to the biologist reader, in an intuitive way, the basic idea of infinitesimal change and differentiation. The second main aim of this chapter is to provide an introduction to ordinary differential equations. The emphasis of this chapter is on usability. By the end of the chapter the reader will be able to formulate basic, but practically useful, differential equations; have a grasp of some basic concepts, including stability and steady states); and will also have an understanding of some basic methods to solve them. Furthermore, the reader will be able to critically evaluate differential equation models she may encounter in the research literature. The more general theoretical introduction into this topic is accompanied by fully worked case studies, including a differential equation model of the spread of malaria and a stability analysis of Cherry and Adler’s bistable switch.
David J. Barnes, Dominique Chu

Chapter 5. Mathematical Tools

Abstract
This chapter describes the use of the free, open-source computer algebra system Maxima. Maxima is a system similar to Maple and Mathematica. The use of Maxima is illustrated by a number of examples. Special emphasis is placed on practical advice on how the software can be used, and the reader is made aware of difficulties and pitfalls of Maxima. The chapter also demonstrates how to use Maxima in practice by walking the reader through a number of examples taken from earlier chapters of the book.
David J. Barnes, Dominique Chu

Chapter 6. Other Stochastic Methods and Prism

Abstract
This chapter introduces the reader to the concept of stochastic systems. It motivates the importance of noise and stochastic fluctuations in biological modeling and introduces some of the basic concepts of stochastic systems, including Markov chains and partition functions. The main objective of this theoretical part is to provide the reader with sufficient theoretical background to be able to understand original research papers in the field. Strong emphasis is placed on conveying a conceptual understanding of the topics, while avoiding burdening the reader with unnecessary mathematical detail. The second part of this chapter describes PRISM, which is a powerful computational tool for formulating, analyzing and simulating Markov-chain models. Throughout the chapter, concepts are illustrated using biologically-motivated case studies.
David J. Barnes, Dominique Chu

Chapter 7. Simulating Biochemical Systems

Abstract
This chapter introduces the reader to the principles underlying Gillespie’s widely-used stochastic simulation algorithm (SSA), for the exact stochastic modeling of chemical reactions involving relatively small numbers of molecules. We also look at Gibson and Bruck’s improvements to the SSA, in order to support larger numbers of reactions, as well as the more recent variation by Slepoy, Thompson and Plimpton. All of these techniques are illustrated with Java implementations and a discussion of their complexity. We also introduce the Dizzy and SGNS2 toolkits, which implement some of these approaches, along with tau-leap approximation and reaction delays.
David J. Barnes, Dominique Chu

Chapter 8. Biochemical Models Beyond the Perfect Mixing Assumption

Abstract
This chapter looks at modeling of biochemical systems when the assumption of perfect mixing is relaxed and spatial configurations of molecules need to be taken into account. Spatial simulations not only introduce additional degrees of freedom in the system, but demand a somewhat different way of thinking about the model. This chapter introduces the reader conceptually to spatial modeling but also contains two walk-through examples. It uses the widely respected Smoldyn simulation software to illustrate the modeling process in spatial systems. The case study in this model is a biochemical change detector.
David J. Barnes, Dominique Chu

Backmatter

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