Faculty Mentors
Examples of research topics and descriptions of faculty mentors' research track records are provided below for each thrust area.
Research Thrust Area 1: Computational Systems Biology
Dr. Laubenbacher is a Research Professor at VBI, Professor of Mathematics in the VT Mathematics Department, and a faculty member in the interdepartmental Ph.D. program “Genetics, Bioinformatics, and Computational Biology (GBCB).” He also holds an adjunct faculty position in the Wake Forest University Department of Cancer Biology. He conducts research in modeling and simulation of biological networks, computational immunology, and bioinformatics tool development.
Example Project: Discrete models. The Laubenbacher group has developed a software package to make top-down phenomenological models of biochemical networks from time courses of experimental data. The models are in the form of time-discrete and state-discrete dynamical systems that carry information about the wiring diagram and dynamics of the network that generated the data. Students will use this software together with two types of time course data. 1) Data generated from a synthetic 59 node network designed by VBI's Mendes group. The synthetic network allows the generation of progressively larger datasets, which one can use to demonstrate the effect of larger datasets on algorithm performance. 2) Transcription time course of wildtype and mutant strains of S. cerevisiae undergoing oxidative stress induced by cumene hydroperoxide. The dataset was generated as part of a collaborative project between the Laubenbacher, Mendes, and Shulaev groups at VBI.
Dr. John Tyson has been a leading figure in theoretical and mathematical biology. In the 1970s and 80s, he published influential papers on chemical oscillations and wave propagation in the Belousov-Zhabotinsky reaction, with important implications for cyclic AMP signaling during slime mold aggregation and for electrical impulse propagation in neuromuscular excitable media. In the 1990s he turned his attention to the control of DNA synthesis, mitosis and cell division, building the first accurate and predictive computational models of the protein interaction network controlling cyclin-dependent kinase activity in yeast cells and frog eggs. He is co-editor and contributor to the first textbook on Computational Cell Biology (Springer-Verlag) and has written well-cited reviews of the field in Nature Rev Molec Cell Biol, BioEssays, and Curr Opin Cell Biol. He has served as President of the Society for Mathematical Biology and as Co-chief Editor of the Journal of Theoretical Biology.
Example Project: Continuous models. Students will choose an aspect of cell physiology 1) that interests them, 2) for which the regulatory genes, proteins and interactions are known in some detail, and 3) for which there is at least one preliminary model in the literature. Students will first reproduce, from scratch, the published model(s) and simulations of experimental data. Then they will press the model in a new direction: revised mechanism, new predictions, bifurcation analysis, etc. Dr. Tyson has used the following examples in past courses: circadian rhythm in Drosophila or Neurospora, cell cycle in frog embryo or fruit fly embryo, p53-Mdm2 oscillations, MAP kinase pathway in frog embryo, NF-kB signaling network, apoptosis (programmed cell death), cyclic AMP signaling in Dictyostelium, calcium signaling in neurosecretory cells, and glycolytic oscillations in yeast cells.
Dr. Yue (Joseph) Wang is an associate professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech and is a core faculty member in SBES. His recent research activities in computational bioinformatics focus on the identification of informative molecular markers via comprehensive characterization of gene expression patterns. The major ongoing projects include molecular analysis of human diseases (cancer, muscular dystrophy, lung disease, neuronal degeneration, obesity) via high-throughput (microarray, proteomics) molecular profiling and integrative data analysis.
Example Project: Genomic data clustering. This project will introduce caBIG-VISDA software as a computational tool for cluster modeling, discovery, and visualization. Biomedical applications include defining new cancer subtypes, constructing hierarchical trees of cancer phenotypes, or discover the correlation between cancer statistics and risk factors.
Research Thrust Area 2: Computational Bio-Imaging
Dr. Joseph Wang's (see above) recent research activities in biomedical imaging focus on multichannel imaging, nonlinear registration, and diagnostic decision support. The major ongoing projects include image-based assessment of risk factors in breast cancer and obesity using MRI. Dr. Wang's new exploration is on multichannel cellular and molecular imaging of biological processes.
Example Project: Blind separation of composite biomarkers in advanced multichannel bio-imaging. This project will introduce various blind source separation methods as a computational tool for dissecting composite biomarker distributions in multispectral cellular imaging and dynamic contrast-enhanced MRI. The focused biomedical applications will be on visual screening of cell types and kinetics modeling of cancer angiogenesis. The project will also integrate intelligent computing, advanced bio-imaging, and bioinformatics.
Dr. Peter Santago is an associate professor in the Department of Biomedical Engineering at the Wake Forest University School of Medicine and is a Primary Faculty Member and Associate Director in SBES. His recent research activities in biomedical imaging focus on pattern recognition, CT colonography, and quantitative tissue characterization using ultrasound. He also applies his expertise in pattern recognition to computational drug discovery, heart-rate variability analysis, MS lesion analysis in MRI, and other general biomedical image analysis problems.
Example Project: Comparison of ultrasound images and signals to histological images of muscle tissue. This project will introduce the student to several aspects of modern biomedical image analysis including: 1) interdisciplinary team work by collaborating faculty and students in basic science to image histological slices and understand muscle tissue; 2) multimodality image analysis, in particular microscope and ultrasound (CT and MRI images may also be available); 3) relating images to cellular level structure; and 4) image analysis software to solve real problems.
Dr. Chris Wyatt is an assistant professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech and is a core faculty member in SBES. He has been conducting research in biomedical image analysis for eight years in the areas of segmentation, registration, morphology, and computer-aided diagnosis. He has published 10 peer-reviewed papers in the area of CT Colonography and related image processing and pattern recognition algorithms. He has an ongoing research program in neuroimage analysis of non-human primates and skeletal phenotyping of small animals using microCT. Dr. Wyatt also has industrial and research expertise in machine vision, including autonomous systems.
Example Project: In-vivo measurement of a skeletal phenotype using microCT. This project will examine the use of microCT as a tool for measuring phenotypes in small animal models of disease. The focus will be on measurement of the baseline total bone volume in a group of mice. The students will be required to develop the imaging protocol, collect preliminary data, estimate the number of animals needed, conduct the final data acquisitions, and make the bone volume measurements. The project integrates with the computational bio-imaging courses by providing practical experience with one of the modalities, including imaging physics, image reconstruction, and radiation safety. The project will also emphasis the concept of imaging as a measurement tool, the design of imaging experiments, and the design and use of computational tools for analysis.
Research Thrust Area 3: Computational Physiology
Dr. J. Wallace Grant is Interim Director of SBES, and a professor in the Department of Engineering Science and Mechanics at Virginia Tech. He was a key participant, along with Drs. Santago and Scott, in the founding of the School. Dr. Grant's research interests are related to mathematical modeling of the inner ear, encoding of linear and angular acceleration of the head, mechanotransduction aspects of receptor cells, and theoretical and experimental work on vestibular mechanics. He has been involved in biomechanics research and teaching since his graduate school training in this area. He developed and taught many biomechanics courses including those that incorporate physiologic principles and apply engineering quantitative analysis.
Example Project: Computational Analysis of the Biomechanics of Vestibular Hair Cells. Students will begin with a qualitative description of a vestibular hair cell by identifying governing physical laws, and creating a simplified mathematical model that quantitatively describes the function of the cell. Students will then explain the relevance of each parameter in the model, perform parametric studies, and draw conclusions about the influences of the parameters in various physiologic situations, particularly in aerospace applications.
Dr. Joel Stitzel is an assistant professor in the Virginia Tech – Wake Forest University School of Biomedical Engineering Sciences, located on the WFU campus. He has appointments in the Wake Forest Institute for Regenerative Medicine, and the Department of Surgery. Dr. Stitzel's primary research interest is in computational modeling, specifically explicit finite element-based biomechanics research. Dr. Stitzel is a leader in the field of computational injury biomechanics, having pioneered the use of Lagrangian-Eulerian modeling methods in injury biomechanics research to study soft tissue injuries. Dr. Stitzel has funding from the Centers for Disease Control's National Center for Injury Prevention and Control to study lung injuries in MVA's and blunt trauma, and funding from Toyota and the National Highway Traffic Safety Administration to study car crash injuries. Both studies utilize experimental measures and outcomes along with modeling techniques to better predict clinical outcomes in trauma victims.
Example Project: Injury prediction using finite element modeling and imaging modalities This project will train the student(s) in relating engineering metrics of stress and strain to clinical measures such as inflammation to study lung injury or another injury of interest. The motivation for the research is to use real world injury data to validate computer models of the injury with a focus on prevention or enhanced understanding of the injury. A range of potential insult severities will be modeled, followed by a study of the finite element based injury metrics of maximum principal strain, von mises strain, and the product of strain and strain rate on the tissue of interest. The volume of tissue affected will be correlated with common measurements of the injury using CT, MRI, or PET (depending on tissue type and mechanism of interest) from the clinical database at WFU. The examples will help to foster interest in real-world injuries and a using a computational approach to understanding the injury severity. As desired and depending on the interest and expertise of the student in physiology, the student may pursue more physiologically based modeling using models of tissue or cellular level response to mechanical strain.
