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<p class="MsoNormal"><span style="font-size:13.5pt;font-family:"Arial",sans-serif"><a href="https://www.chee.uh.edu" target="_blank"><span style="color:#C8102E;text-decoration:none"><img border="0" width="600" height="165" style="width:6.2541in;height:1.7208in" id="_x0000_i1025" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/sa_header.png" alt="William A. Brookshire Department of Chemical and Biomolecular Engineering Seminar Series"></span></a><o:p></o:p></span></p>
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<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-family:"Arial",sans-serif">Systems Biology Approaches for Predicting Cancer Cell Behaviors and Rationally Designing Therapeutic Interventions<o:p></o:p></span></b></p>
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<p class="MsoNormal" align="center" style="text-align:center"><b><span style="font-family:"Arial",sans-serif">Matthew J. Lazzara, Ph.D.</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"><br>
</span><b><span style="font-family:"Arial",sans-serif">Professor of Chemical Engineering & Biomedical Engineering<br>
University of Virginia<o:p></o:p></span></b></p>
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<strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif">F</span></strong><strong><span style="font-size:12.0pt;font-family:"Calibri",sans-serif">riday,
</span></strong><strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif">Sept 6 2024 | 10:30a
</span></strong><strong><span style="font-size:12.0pt;font-family:"Calibri",sans-serif">Central</span></strong><b><span style="font-size:12.0pt;font-family:"Arial",sans-serif"><br>
</span></b>Engineering room L2D2<span style="font-size:12.0pt;font-family:"Arial",sans-serif"><o:p></o:p></span></p>
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<strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E">LECTURE ABSTRACT</span></strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Arial",sans-serif">The design of effective combination therapies for cancer depends on identifying the druggable signaling pathways that transformed cells use to make phenotypic decisions. Finding these pathways
is challenging because signaling information is high-dimensional and because cancer cells exhibit substantial cell-to-cell variability and phenotypic plasticity in the heterogeneous tumor microenvironment. Overcoming these barriers requires the careful integration
of appropriate computational models with experimental data that describe the complexity of tumor biology at multiple scales. This talk will focus on our integration of data science with mechanistic modeling approaches to understand how pancreas cancer cells
make decisions leading to chemoresistance in response to diverse cues in the tumor microenvironment. Models are trained on biochemical measurements and high-content imaging that capture the dynamics and heterogeneity of the multivariate signaling processes
leading to cell phenotype determination. Model predictions are tested in mouse models and cell culture experiments and are further validated through the analysis of human patient data. Our results nominate specific candidate drug combinations as potentially
more effective pancreas cancer treatments and demonstrate how cancer systems biology approaches can be successfully deployed for the effective design of preclinical and clinical studies.
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<strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E">SPEAKER BIOSKETCH</span></strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E"><o:p></o:p></span></p>
<p class="MsoNormal" style="text-align:justify"><span style="font-family:"Arial",sans-serif">Dr. Matthew Lazzara is Professor of Chemical Engineering and Biomedical Engineering at the University of Virginia, UVA Shannon Center Mid-Career Faculty Fellow, Co-Director
of the UVA Cancer Systems Biology U54 Center, and a member of the UVA Comprehensive Cancer Center. He received a B.S. in Chemical Engineering with highest honors from the University of Florida and a Ph.D. in Chemical Engineering from the Massachusetts Institute
of Technology. He remained at MIT for postdoctoral studies in Biological Engineering and was the recipient of an NIH Ruth L. Kirschstein National Research Service Award Postdoctoral Fellowship. Work in the Lazzara Lab employs integrated experimental and computational
methods to study cancer cell signaling and has been funded by the National Institutes of Health, National Science Foundation, and American Cancer Society. Projects focus on the rational, model-driven identification of combination therapies for cancer and on
fundamental studies of the spatiotemporal regulation of cell signaling by phosphatases and receptor trafficking. Dr. Lazzara is the prior recipient of the American Cancer Society Research Scholar Grant and several teaching awards, including the S. Reid Warren,
Jr. Award and the Outstanding Faculty Award of the AIChE Delaware Valley. He is a standing member of the NIH
<i>Tumor Evolution, Heterogeneity and Metastasis</i> study section, editorial board member of
<i>Cellular and Molecular Bioengineering</i> and <i>Frontiers in Systems Biology</i>, and recent co-chair of the NCI Cancer Systems Biology Consortium steering committee.<b><o:p></o:p></b></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif"> <o:p></o:p></span></p>
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<em><span style="font-size:8.5pt;font-family:"Arial",sans-serif">This is an official message sent by the William A. Brookshire Department of Chemical & Biomolecular Engineering.</span></em><span style="font-size:8.5pt;font-family:"Arial",sans-serif"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:13.5pt;font-family:"Arial",sans-serif"><a href="https://www.chee.uh.edu" target="_blank"><span style="color:#C8102E;text-decoration:none"><img border="0" width="600" height="165" style="width:6.2541in;height:1.7208in" id="_x0000_i1027" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/sa_footer.png" alt="William A. Brookshire Department of Chemical and Biomolecular Engineering"></span></a><o:p></o:p></span></p>
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