An Invitation to BiomathematicsBy
- Raina Robeva, Sweet Briar College, VA, USA
- James Kirkwood, Sweet Briar College, Virginia, U.S.A.
- Robin Davies, Sweet Briar College, Virginia, U.S.A.
- Leon Farhy
- Boris Kovatchev, UVA Health System, U.S.A.
- Martin Straume, University of Virginia, Center for Biomathematical Technology, U.S.A.
- Michael Johnson, University of Virginia Health Sciences Center, Charlottesville, USA
Essential for all biology and biomathematics courses, this textbook provides students with a fresh perspective of quantitative techniques in biology in a field where virtually any advance in the life sciences requires a sophisticated mathematical approach. An Invitation to Biomathematics, expertly written by a team of experienced educators, offers students a solid understanding of solving biological problems with mathematical applications. This text succeeds in enabling students to truly experience advancements made in biology through mathematical models by containing computer-based hands-on laboratory projects with emphasis on model development, model validation, and model refinement.The supplementary work, Laboratory Manual of Biomathematics is available separately ISBN 0123740223, or as a set ISBN: 0123740290)
This textbook is intended for use within courses in mathematical biology, as well as in general mathematics and biology courses that cover the application of math and statistics to biological problems. The reader is not expected to have any extensive background in either math or biology.
Hardbound, 480 Pages
Published: October 2007
Imprint: Academic Press
- 1. Processes that Change with Time: Introduction to Dynamical Systems / 2. Complex Dynamics Emerging from Interacting Dynamical Systems / 3. Mathematics in Genetics / 4. Quantitative Genetics and Statistics / 5. Risk Analysis of Blood Glucose Data / 6. Predicting Septicemia in Neonates / 7. Cooperative Binding: How Your Blood Transports Oxygen / 8. Ligand Binding, Data Fitting, and Least-Squares Estimates of Model Parameters / 9. Endocrinology and Hormone Pulsatility / 10. Endocrine Network Modeling: Feedback Loops and Hormone Oscillations / 11. Detecting Rhythms in Confounded Data / 12. Using Microarrays to Study Gene Expression Patterns