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The Washington Research Foundation Fellowship

Gargi Chakraborty, Neurobiology and Biochemistry - 2007-08 RFAU

Gargi Chakraborty, 2007 RFAU recipientMy interest in studying Neurobiology stems from a qualitative research study on Alzheimer’s disease that I undertook in high school. At the University of Washington, I had the opportunity to conduct quantitative research by applying mathematical modeling to brain physiology. In Dr. Swanson’s Neuropathology lab, my primary investigation focuses on assessing glioma (brain tumor) growth in vivo through multimodal clinical imaging. In the two and a half years I have worked in this lab, Dr. Swanson has served not only as my research mentor, but also as a personal guide who has exposed me to various avenues of investigative learning. This learning serves as a pillar in my pursuit of a medical profession. As a future physician and biomedical researcher, I hope to apply the insights I have gained from my research work to study human disorders by combining non-invasive clinical techniques and mathematical modeling.

Mentor: Dr. Kristin R. Swanson, Department of Pathology

Project Title: Spatio-temporal modeling of hypoxia and glucose metabolism in human gliomas

Abstract: Mathematical modeling presents a new tool to understand time and space dependent functions in biology. Modeling the growth of gliomas, which are primary brain tumors, involves quantifying their diffuse and proliferative capacity. These properties can be assessed in vivo through non-invasive imaging techniques, which include Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). The current mathematical model developed by Dr. Kristin Swanson is based upon the Fisher equation and utilizes MRI scans from two time points to determine velocity of tumor growth. The purpose of this study is to enhance the Swanson model by incorporating metabolism within and outside the clinically defined tumor region. Two characteristic metabolic activities exhibited by gliomas include up-regulation of glycolysis and hypoxia (state of low oxygenation). These functional activities can be quantified via PET imaging using [18F]-Fluorodeoxyglucose (FDG) to monitor glucose uptake and [18F]-Fluoromisonidazole (FMISO) to monitor hypoxia. Using combinatorial imaging for in vivo studies, we have spatially localized regions of excessive glycolysis and oxygen depletion. Currently, we are developing a model to characterize spatial and temporal changes in peak metabolic activity driving tumor growth. This model incorporates heterogeneity of grey and white matter within the brain and the microenvironment of the tumor. Our goal is to develop simulations that mimic future FDG and FMISO PET scans based on original PET imaging conducted at the time of diagnosis. This mathematical model will allow early assessment of tumor behavior and prediction of its spatio-temporal spread enabling patient-directed treatment of gliomas.