Graduate portfolio programs provide opportunities for enrolled graduate students to obtain transcriptable credentials in cross-disciplinary academic areas of inquiry while they are completing the requirements for a master’s or doctoral degree in a particular discipline. Portfolio programs promote cross-disciplinary scholarship and study by bringing together faculty and students from a variety of disciplines whose interests transcend boundaries of traditional academic disciplines. The Office of Graduate Studies provides an overview of the university’s general portfolio program requirements.
Computational Medicine is an emerging discipline that uses physics-based and data-driven advanced mathematical approaches to model complex systems across a spectrum of scales, from the molecular to cellular, to the organ to system levels of the human body, and even to the entire health care system. To accurately represent these complex systems, such modeling efforts need to capture the individuality of health and disease for accurate decision making at all levels, ranging from patient to policy. To do so requires substantial computational and mathematical skills, as well as detailed medical knowledge. The models can be theory-driven, knowledge-driven, or data-driven, or typically a novel combination of these.
The Computational Medicine Portfolio Program is intended for graduate students with strong mathematical and physical science backgrounds, but limited knowledge of biology and/or medicine, to pursue a program of study that will prepare them to interact knowledgably and collaborate productively with members of the medical community on interdisciplinary, cutting-edge research. It is important to realize that medicine itself is an enormous field, consisting of numerous subdisciplines, and Computational Medicine has become an important research direction in most of the larger subdisciplines. For these reasons, the Computational Medicine Portfolio will allow for the maximum flexibility to suit students whose interests reside within specific subdisciplines, which are currently Cardiovascular, Oncology and Neurology.
| General Topics | |
|---|---|
| CSE 397.9 | Mathematical Physiology |
| BME 383J.13 or CSE 397.7 | Introduction to Mathematical and Physical Biology |
| BME 385J.6 | Analysis of Biological Systems |
| Medical Imaging | |
| BME 381J.3 or CSE 397.8 or ECE 385J.18 | Biomedical Imaging: Signals/Systems |
| ECE 381V or CSE 397 | Computational Magnetic Resonance Imaging |
| Cardiovascular | |
| BME 385J or CSE 397.4 | Computational Modeling of the Cardiovascular System |
| BME 384T.2 or M E 385J or CSE 397.5 | Cellular, Tissue, and Scaffold Biomechanics |
| KIN 395.11 | Pulmonary Exercise Physiology |
| KIN 395.16 | Cardiovascular Response to Exercise |
| KIN 395.70 | Human Cardiovascular and Autonomic Physiology |
| KIN 395.62 | Aging and Cardiovascular Function and Disease Risk |
| Oncology | |
| BME 393J.3 or CSE 397.6 | Introduction to Computational Oncology |
| BIO 394T | Tumor Biology |
| PGS 384L | Biochemical and Molecular Toxicology |
| PGS 388K | Molecular Mechanisms/Methods in Nutrition and Cancer |
| Neurology | |
| NEU 394P or C S 395T | Neural Computation |
| NEU 380E or PSY 380E | Vision Systems |
| NEU 382T | Principles of Neuroscience I |
| Medical Sciences | |
| KIN 395 | Human Anatomy and Dissection |
The following undergraduate courses are recommended for students who wish to strengthen their background, but these courses cannot be counted as part of the Portfolio:
Professor Michael Sacks, Portfolio Program Chair, msacks@oden.utexas.edu
Stephanie Rodriguez, Graduate Program Administrator, slr@oden.utexas.edu