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Course Descriptions
Sample of BME Course Offerings
BME Courses are offered within the three technical areas:
Area 1 Cellular and Molecular Imaging
Area 2 Cellular and Bio-Molecular Engineering
Area 3 Computation Biomedical Engineering
The Graduate Advisor helps student identify suitable courses in each area.
For course locations and times of current offerings, please see the UT Online Course Schedule.
For descriptions of other BME and non-BME courses, please see the UT Course Catalog.
- BME 380J, Topic 5 Biostatistics, Study Design and Research Methods
Principles for hypothesis testing; confidence limits; regression analysis; correlation; analysis of variance; experimental design and factorial analysis; discriminate analysis; applications of statistics. Taught via distance learning methods between UT Austin and UTHSC-H.
Prerequisite: Passing grade on MATLAB and Probability exams.
- BME 381J, Topic 1 Laser-Tissue Interaction: Thermal
The thermal response of random media in interaction with laser irradiation. Calculation of the rate of heat production caused by direct absorption of the laser light, thermal damage, and ablation.
- BME 381J, Topic 3 Biomedical Imaging: Signals and Systems
Physical principles and signal processing techniques used in thermographic, ultrasonic, and radiographic imaging, including image reconstruction from projections such as CT scanning, MRI, and millimeter wave determination of temperature profiles.
- BME 381J, Topic 4 Optical Spectroscopy
Measurement and interpretation of spectra: steady-state and time-resolved absorption, fluorescence, phosphorescence, and Raman spectroscopy in the ultraviolet, visible, and infrared portions of the spectrum.
- BME 381J, Topic 7 Digital Image and Video Processing
Digital image acquisition, processing, and analysis; algebraic and geometric image transformations; two-dimensional Fourier analysis; image filtering and coding.
- BME 382J, Topic 1 Cell and Tissue Engineering
Use of case studies to explore pathologies of tissue, current clinical treatment, and the role of engineers in developing new technologies to diagnose and treat these pathologies. Emphasis on the use of quantitative cellular and molecular techniques. Applications of synthetic and natural biomaterials.
- BME 382J, Topic 4 Advanced Engineering Biomaterials
Overview of biomaterials, including prosthetics, ceramics, metal implants, and polymers, with specific emphasis on properties and applications. The immunology of material-tissue interactions and the issues of biocompatibility.
- BME 382J, Topic 6 Biopolymers and Drug/Gene Delivery
Biomedical polymers and their applications in drug delivery and gene therapy. Emphasis on parenteral, mucosal, and topical delivery of biomolecules, and the role of polymers in genetic therapy and DNA vaccination.
- BME 383J Advanced Modeling of Biomolecules
Advanced computational techniques for modeling biomolecular structure, dynamics and interactions. A focus on sophisticated force field methods for modeling molecular interactions such as electrostatic force, development of simulation algorithms for smart sampling of thermodynamic and kinetic properties, and current applications to biomedicine and biotechnology.
- BME 383J, Topic 4 Biomechanics of Human Movement
- BME 383J, Topic 5 Introduction to Nonlinear Dynamics in Biological Systems
Basic concepts of nonlinear mathematics and their application to biological systems.
- BME 383J, Topic 7 Data Mining
Analyzing large data sets for interesting and useful information; online analytical processing, finding association rules, clustering, classification, and function approximation; scalability of algorithms and real-life applications.
- BME 384J, Topics 1 and 2 Biomedical Instrumentation I and II
I: Application of electrical engineering techniques to analysis and instrumentation in biological sciences: pressure, flow, temperature measurement; bioelectrical signals; pacemakers; ultrasonics; electrical safety; electrotherapeutics.
II: Design, testing, patient safety, electrical noise, biomedical measurement transducers, therapeutics, instrumentation electronics, microcomputer interfaces, and embedded systems.
- BME 384J, Topic 6 Neurophysiology/Prosthesis Design
The structure and function of the human brain. Discussion of selected neurological diseases in conjunction with normal neurophysiology. Study of neuroprosthesis treatments and design philosophy, functional neural stimulation, and functional muscular stimulation.
- BME 385J Biomedical Micro- and Nanotechnology
- BME 385J, Topic 36 Engineering Applications of Immunology and Disease Pathology
- BME 387J Nanomedicine in Healthcare
Taught via distance learning methods between UT Austin and UTHSC-H.
- BME 387J Synaptic Basis for Learning and Memory
Taught via distance learning methods between UT Austin and UTHSC-H.
- BME 387J Mathematical Methods for Health Informatics, Biomedical Science and Engineering
Advanced mathematics for graduate students and faculty in biomedical engineering and health information sciences. The topics presented in this class will be particularly useful for students in the computational biomedicine and biomedical engineering focus areas. Taught via distance learning methods between UT Austin and UTHSC-H.
- BME 387J Numerical Methods for Health Information Sciences, Biomedical Science and engineering
Introduction to numerical methods for graduate students and faculty in biomedical engineering and health information sciences. The topics presented in this course will be particularly useful to students in the computational biomedicine and biomedical engineering focus areas. Taught via distance learning methods between UT Austin and UTHSC-H.
- BME 387J Mathematical Modeling of Biological Systems and Disease
Modeling tools from mathematical and computational biomedicine for graduate students and faculty in biomedical engineering and health information science. Taught via distance learning methods between UT Austin and UTHSC-H.
- BME 387J Statistical Methods in Bioinformatics
Introduction to the concepts and statistical methods for analyzing large-scale biological data generated from emerging genomic and proteomic techniques. The statistical methods covered include dynamic programming, maximum likelihood estimation, Bayesian inference, Hidden Markov Models, Markov chain Monte Carlo, classification and clustering methods. Taught via distance learning methods between UT Austin and UTHSC-H.
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