Date(s) - 02/20/2023
3:00 pm - 4:00 pm
Tumors thrive on heterogeneity. Computational models that account for complex tumor microenvironments and cell state plasticity are a crucial component for better understanding disease progression. Single-cell genomics data offer powerful new means to constrain models to data and make predictions. We develop models of complex tumor dynamics in light of single-cell data. We study the role of myeloid-derived suppressor cells in metastatic breast cancer with stochastic delay differential equations. Informed by clinical response data, we fit models to individual patient data and discover that inhibition of NK cells plays a critical role in determining response. We study epithelial-mesenchymal transition across cancers and stimuli, fitting models through Bayesian parameter inference to identify genes that mark for the dynamics of intermediate cell state transitions. Overall, we argue that combination of dynamical systems modeling with statistical inference on single-cell genomics data provides unique opportunities to learn the cell state dynamics underlying disease and predict new tumor biomarkers.
Adam MacLean studies cell fate decision-making in stem cells and cancer. He has developed methods for cell-cell communication network inference and models of gene regulatory dynamics constrained by single-cell genomics data via statistical inference. He is an Assistant Professor in the Department of Quantitative and Computational Biology at the University of Southern California. He completed a PhD at Imperial College London, followed by postdoc positions at the University of Oxford and the University of California, Irvine. Awards for his work since starting a lab at USC in 2019 include an NSF CAREER award and an early-career NIH MIRA award.