Książki

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)

• Provides a complete guide for development of quantification skills crucial for applying mathematical methods to biological problems
• Includes well-known examples from across disciplines in the life sciences including modern biomedical research
• Explains how to use data sets or dynamical processes to build mathematical models
• Offers extensive illustrative materials
• Written in clear and easy-to-follow language without assuming a background in math or biology
• A laboratory manual is available for hands-on, computer-assisted projects based on material covered in the text

An Invitation to Biomathematics

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