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Dynamic Models in Biology
Stephen P. Ellner, John Guckenheimer
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Naturwissenschaften, Medizin, Informatik, Technik / Naturwissenschaften allgemein
Beschreibung
From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology.
Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians.
Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
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Approximation, Rate equation, Diagram, Action potential, Addition, Linear algebra, Theory, Regulation of gene expression, Case study, Simulation, Population model, Derivative, Probability distribution, Michaelis–Menten kinetics, Sensitivity analysis, Population size, Fecundity, State variable, Requirement, Summation, Parameter, Variance, Variable (mathematics), Phase portrait, Vector field, Calculation, Eigenvalues and eigenvectors, Measurement, Molecule, Equilibrium point, Computational model, Differential equation, Prediction, Biology, Histogram, Error, Result, Computer simulation, Division by zero, Linearization, Two-dimensional space, Estimation, Coefficient, Quantity, Diagram (category theory), Mathematical analysis, Protein, Chemical reaction, Initial condition, Markov chain, Random variable, Boundary value problem, Accuracy and precision, Measles, Vaccination, Probability, Voltage clamp, Expected value, Enzyme kinetics, Organism, Bifurcation theory, Normal distribution, Law of mass action, Disease, Equation, Numerical analysis, Mortality rate, Experimental data, Stochastic, Mathematics