BME 33400/ 3 Cr.
This course explores computational approaches to analyzing biological data and solving biological problems. Students will fit and interpret biological data, apply probabilistic and differential equation modeling techniques to biological processes, and assess numerical tools for biomedical applications. Special attention is given to the built-in analysis functions of MATLAB.
- Available Online: No
- Credit by Exam: No
- Laptop Required: No
P: MATH 26100, MATH 26600, and ENGR 29700.
Numerical Methods in Biomedical Engineering by Stanley M. Dunn, Alkis Conastantinides and Prabhas V. Moghe (2006), Academic Press
This course presents an engineering toolbox of computational approaches for solving common problems in biomedical engineering. It serves as an introduction not only to the methods themselves but to the strengths, drawbacks, and trade-offs of each, providing the student with the background needed not only to apply these computational tools but to recognize the conditions under which such tools are effective and appropriate. Furthermore, it introduces the students to such fundamental biomedical topics as ion channels and nerve cell potentials; cardiovascular dynamics; enzyme-substrate interaction; and DNA/protein sequence matching and classification.
Upon completion of this course, the student should be able to:
- Fit linear and nonlinear curves to data using least squares method.
- Interpolate data.
- Use MATLAB to solve a linear system of equations.
- Find the roots of an equation using the Newton-Raphson method.
- Use the Michaelis-Menten model to predict substrate concentrations in an enzyme reaction.
- Model a dynamic biological process using differential equations.
- Solve a system of differential equations using an appropriate numerical integrator.
- Predict nerve cell potentials.
- Use a Markov model to describe ion channel gating.
- Find database matches to DNA sequences.
BME33400 introduces several types of numerical tools, broadly classified into four categories:
- Fitting, analyzing and interpolating existing data with analytic functions.
- Using differential equations to describe biological processes, and selecting appropriate numerical tools to manipulate and solve those equations.
- Stochastic processes and probabilistic process modeling, including discussion of Markov and Monte Carlo simulations.
- Introduction to database search tools, text-string data processing, and other bioinformatics topics.