Numerical approaches for prediction of cancer therapeutic efficacy
Interdisciplinary Areas: | Engineering and Healthcare/Medicine/Biology |
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Project Description
Patients diagnosed with breast cancer receive treatments based on a range of different criteria such as the stage of the disease or the hormone receptor status. However, these treatment strategies do not work for all patients, and clinicians are left with limited data sets to guide treatment plans. A patient’s cancer cells would ideally be expanded in vitro using protocols akin to those used for the selection of antibiotics for the treatment of bacterial infections. Unfortunately, traditional cell culture techniques tend to preferentially select for cancer cell populations that are more adept at surviving in 2-D tissue culture conditions, leading to a loss of predictive capacity for the system. Furthermore, 2-D monolayer cultures are unable to capture the complex, distinct 3-D arrangements that the cancer cells develop with adjacent cells and with the extracellular matrix (ECM) within a tumor. Both factors drastically alter the cancer cell response to a drug. This collaborative project uses a combination of experimental and numerical approaches to unravel mass transfer processes and cell responses in engineered 3D microenvironments that mimic key aspects of the tumor in order to develop tools to guide therapeutic decisions and improve patient outcomes.
Start Date
April 15, 2019
Postdoc Qualifications
Planning and executing projects in collaboration with the PI and collaborators
Develop the experimental design for the project to include wet lab work as well as numerical approximations
Perform data analysis using tools available in the lab as well as the development of new tools as necessary
Prepare manuscripts and contribute preliminary data for grant proposals
Supervise graduate students and undergraduate students in conducting sample preparation, experimental design, and data analysis
Co-advisors
Arezoo Ardekani, ardekani@purdue.edu, Mechanical Engineering