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Mathematical Models in Biology: An Introductionby John A. Rhodes
Synopses & Reviews
Focusing on discrete models across a variety of biological subdisciplines, this introductory textbook includes linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction from DNA sequence data, genetics, and infectious disease models. Assuming no knowledge of calculus, the development of mathematical topics, such as matrix algebra and basic probability, is motivated by the biological models. Computer research with MATLAB is incorporated throughout in exercises and more extensive projects to provide readers with actual experience with the mathematical models.
Book News Annotation:
Allman (math and statistics, U. of Southern Maine) and Rhodes (math, Bates College) share a strong interest in the interaction between the mathematical and biological sciences, as well as in the education of undergraduates for whom they've prepared this introductory text. Covering discrete models across a variety of biological subdisciplines, the authors aim to intrigue students rather than overwhelm them; they write in an informal style and make no attempt to offer definitive coverage, although they do treat some topics not usually touched on at this level. Coverage includes linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction, genetics, and infectious disease models. Math topics are introduced as needed for the biological topics; knowledge of calculus is not prerequisite, but inclusion of material for those who do have that background makes the text adaptable. Computer investigations with MATLAB are incorporated throughout. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)
Linear and non-linear models of populations, molecular evolution, phylogenetic tree construction, genetics, and infectious diseases are presented with minimal prerequisites.
Table of Contents
1. Dynamic modeling with different equations; 2. Linear models of structured populations; 3. Non-linear models of interactions; 4. Modeling molecular evolution; 5. Constructing phylogenic trees; 6. Genetics; 7. Infectious disease modeling; 8. Curve fitting and biological modeling; Appendix A. Basic analysis of numerical data; Appendix B. For further reading.
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