Mathematical Models of Biological Systems

Mathematical Models of Biological Systems

by Hugo van den Berg
Pub. Date:
Oxford University Press


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Mathematical Models of Biological Systems

Mathematical Models of Biological Systems provides a practical introduction to basic mathematical modelling methodology and analysis. It covers a variety of biological applications and uses these topics in turn to highlight key components in the art of modelling. Its primary aim is to give students the tools to translate simple, real-world biological problems into rigorous mathematical models. A secondary aim is to teach the reader how to critically assess the modelling components in the primary life science literature.

The book covers deterministic as well as stochastic dynamics, continuous-time as well as discrete-time dynamics, partial differential equations, dimensional analysis, and curve fitting/parameter estimation. It contains numerous case studies, graded from elementary examples to more complicated problems, as well as a general treatment of good modelling practice. Although the book assumes a basic background in mathematics, specifically beginning calculus and elementary statistics, all requisite material is included in a series of appendices.

Product Details

ISBN-13: 9780199582181
Publisher: Oxford University Press
Publication date: 01/22/2011
Pages: 256
Product dimensions: 7.40(w) x 9.60(h) x 0.60(d)

About the Author

Hugo van den Berg obtained an MSc in neurophysiology and molecular endocrinology from the Free University of Amsterdam and a PhD in theoretical ecology from the same university. He became a Research Fellow and later a lecturer in Mathematical Biology at the University of Warwick. His chief research interests are specificity of immune recognition, nutrient fluxes, energy balance in individuals and ecosystems, regulation of contractions in childbirth, and receptor signalling.

Table of Contents

1. What models can do for the life sciences
2. Basic modelling concepts and techniques
3. Working with Ordinary Differential Equations
4. Models and data analysis
5. Modelling principles
6. Growth of populations and of individuals
7. Infection and immunity
8. Physiology
9. Stochastic models Appendix A: Maths miscellany Appendix B: From Boltzmann to Nernst Appendix C: Ultimate behaviour of a closed, connected, compartmental system Appendix D: Buckingham's theorem Appendix E: Minimising the sum of squares with respect to the parameters Appendix F: Global sensitivity analysis: parameter transformations for 'large' systems Index

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