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A Course in Mathematical Biology数学生物学教程:有数学与计算方法的定量模型

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A Course in Mathematical Biology数学生物学教程:有数学与计算方法的定量模型

最 低 价:¥470.90

定 价:¥627.83

作 者:Gerda de Vries 著

出 版 社:Oversea Publishing House

出版时间:2006-6-1

I S B N:9780898716122

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内容简介

The field of mathematical biology is growing rapidly. Questions about infectious diseases, heart attacks,cell signaling, cell movement, ecology, environmental changes, and genomics are now being analyzed using mathematical and computational methods. A Course in Mathematical Biology: Quantitative Modeling with Mathematical and Computational Methods teaches all aspects of modern mathematical modeling and is specifically designed to introduce undergraduate students to problem solving in the context of biology.
Divided into three parts, the book covers basic analytical modeling techniques and model validation methods; introduces computational tools used in the modeling of biological problems; and provides a source of open-ended problems from epidemiology, ecology, and physiology. All chapters include realistic biological examples, and there are many exercises related to biological questions. In addition,the book includes 25 open-ended research projects that can be used by students. The book is accompanied by a Web site that contains solutions to most of the exercises and a tutorial for the implementation of the computational modeling techniques. Calculations can be done in modern computing languages such as Maple, Mathematica, and MATLAB.
Intended for upper level undergraduate students in mathematics or similar quantitative sciences,A Course in Mathematical Biology: Quantitative Modeling with Mathematical and Computational Methods is also appropriate for beginning graduate students in biology, medicine, ecology, and other sciences.It will also be of interest to researchers entering the field of mathematical biology.

作者简介

Gerda de Vries is Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada.

目录

Preface
Ⅰ Theoretical Modeling Tools
 1 Introduction
  1.1 The Modeling Process
  1.2 Probabilities and Rates
  1.3 Model Classes
  1.4 Exercises for Modeling
 2 Discrete-Time Models
  2.1 Introduction to Discrete-Time Models
  2.2 Scalar Discrete-Time Models
  2.2.1 Growth of a Population and the Discrete Logistic Equation
  2.2.2 Cobwebbing, Fixed Points, and Linear Stability Analysis
  2.2.3 Analysis of the Discrete Logistic Equation
  2.2.4 Alternatives to the Discrete Logistic Equation
  2.2.5 Models in Population Genetics
  2.3 Systems of Discrete-Time Equations
  2.3.1 Love Affairs: Introduction
  2.3.2 Fixed Points and Linear Stability Analysis for Systems of Discrete-Time Equations
  2.3.3 Love Affairs: Model Analysis
  2.3.4 Host-Parasitoid Models
  2.4 Exercises for Discrete-Time Models
 3 Ordinary Differential Equations
  3.1 Introduction to ODEs
  3.2 Scalar Equations
  3.2.1 The Picard-Lindeltf Theorem
  3.3 Systems of Equations
  3.3.1 Reaction Kinetics
  3.3.2 A General Interaction Model for Two Populations
  3.3.3 A Basic Epidemic Model
  3.3.4 Nondimensionalization
  3.4 Qualitative Analysis of 2x2 Systems
  3.4.1 Phase-Plane Analysis: Linear Systems
  3.4.2 Nonlinear Systems and Linearization
  3.4.3 Qualitative Analysis of the General Population Interac-tion Model
  3.4.4 Qualitative Analysis of the Epidemic Model
  3.5 General Systems of Three or More Equations
  3.6 Discrete-Time Models from Continuous-Time Models
  3.6.1 Numerical Methods
  3.6.2 The Time-One Map
  3.7 Elementary Bifurcations
  3.7.1 Saddle-Node Bifurcation
  3.7.2 Transcritical Bifurcation
  3.7.3 Pitchfork Bifurcation
  3.7.4 Hopf Bifurcation
  3.7.5 The Spruce Budwonn Model
  3.8 Further Reading
  3.9 Exercises for ODEs
 4 Partial Differential Equations
  4.1 Partial Derivatives
  4.2 An Age-Structured Model
  4.2.1 Derivation
  4.2.2 Solution
  4.3 Reaction-Diffusion Equations
  4.3.1 Derivation of Reaction-Diffusion Equations
  4.3.2 The Fundamental Solution
  4.3.3 Critical Domain Size
  4.3.4 Traveling Waves
  4.4 Further Reading
  4.5 Exercises for PDEs
 5 Stochastic Models
  5.1 Introduction
  5.2 Markov Chains
  5.2.1 A Two-Tree Forest Ecosystem
  5.2.2 Markov Chain Theory
  5.2.3 The Princeton Forest Ecosystem
  5.3 Working with Random Variables
  5.3.1 Probability Density
  5.3.2 Probability Mass
  5.3.3 Descriptive Statistics
  5.3.4 The Generating Function
  5.4 Diffusion Processes
   ……
 6 Cellular Automata and Related Models
 7 Estimating Parameters
Ⅱ Self-Giuided Computer Tutorial
 8 Maple Course
Ⅲ Projects
 9 Project Descriptions
 10 Sloved Projects
Appendix:Further Reading
Bibliography
Author Index
Index

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