February 23
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12:00 PM
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1:00 PM
From Cellular Networks to Therapeutic Predictions: A Data-Driven Approach to Precision Medicine
Shawn M. Gomez, EngScD
Professor and Associate Chair for Research
Co-Executive Director, FastTaCS, NC TraCS Institute
Lampe Joint Department of Biomedical Engineering at UNC-Chapel Hill and NC State University
Abstract:
Precision medicine aims to tailor prevention, diagnosis, and therapy to individual patients’ biological states. We pursue this as a multiscale problem, combining molecular and systems biology approaches with translational AI methods to improve clinical decision-making. In this talk, I focus on our systems-level efforts to predict targeted therapeutic responses in cancer. This challenge is particularly acute because despite extensive molecular profiling capabilities, predicting how therapies affect cellular phenotypes remains a critical barrier to precision oncology. Targeted therapies produce highly variable outcomes due to the adaptive, networked nature of cellular signaling. Comprising over 500 kinases, the protein kinome forms the backbone of these networks and represents a central therapeutic target space. However, predicting how kinome perturbations propagate through cellular systems to shape phenotypic outcomes is a major challenge. My research program addresses this by developing data-driven approaches that link kinase inhibition states to downstream cellular responses, enabling the rational design of single-agent and combination therapeutic strategies. I will discuss our work building predictive models that forecast cellular responses to kinase-targeted therapies, validated experimentally across breast and pancreatic cancer cell lines and patient-derived xenograft models. These models integrate large-scale proteomic and multi-omic data within machine learning frameworks to identify key kinases and network features driving therapeutic outcomes. This work illustrates how systems-level modeling translates molecular data into actionable insights for precision medicine. I’ll conclude by highlighting opportunities for research, educational, and translational innovation in BME at UW-Madison.
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