The Herbert and Florence Irving Institute for Cancer Dynamics will continue its seminar series on the topic of mathematical sciences underpinning cancer research. On Wednesday, February 12th (2:00 PM ET), IICD welcomes Franziska Michor, Professor of Computational Biology, Harvard University and Dana-Farber Cancer Institute. Seminar hosted by Simon Tavaré. The seminar will be virtual only. Registration link: https://columbiauniversity.zoom.us/meeting/register/tJMqcOiurTktGNAHvyuKG9I94GN8tW7Gwn8Z
Title: Quantitative Approaches to Understanding Cell Evolution and Treatment Response
Abstract: The Michor lab is interested in cancer evolution through the use of integrative data science, experimental, clinical, and population science approaches. We develop methodology to develop, analyze, and integrate diverse data types and design predictive algorithms using approaches from applied mathematics, statistics, bioinformatics, and machine learning. During this talk we will present two projects: Understanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. To address the limitations of traditional dose-response models and their metrics, we developed Bayesian Estimation of STochastic processes for Dose-Response (BESTDR), a novel framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR uses branching processes to model cell growth and estimates rates as functions of drug concentration. We validated BESTDR with synthetic and experimental datasets, demonstrating its robustness and accuracy in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic and other effects, BESTDR enhances dose-response studies, facilitating robust drug comparisons and mechanism-specific analyses. Single-cell RNA sequencing (scRNA-seq) provides static snapshots of cellular states but cannot directly capture continuous changes in gene expression over time. To overcome this limitation, we have employed and developed computational methods based on optimal transport (OT) analysis to reconstruct single-cell trajectories from scRNA-seq time-series data. In this talk, we will present our previous work employing Waddington OT analysis to infer past trajectories of divergent cell fates during TGF-beta-induced epithelial-to-mesenchymal transition (EMT) in the MCF10A cell line. Additionally, we will introduce a newly developed computational framework that utilizes Lipschitz-regularized Wasserstein gradient flow to reconstruct continuous single-cell trajectories from discrete scRNA-seq time-series data. Validated on synthetic datasets and an in vitro time-series dataset, this method offers a robust and efficient tool for investigating dynamic gene expression changes at single-cell resolution.
Bio: Dr. Michor is the Charles A. Dana Chair in Human Cancer Genetics at the Dana-Farber Cancer Institute and a Professor of Computational Biology and of Stem Cell and Regenerative Biology at Harvard University. She is the director of the Dana-Farber Cancer Institute Center for Cancer Evolution. She has been the recipient of the Theodosius Dobzhansky Prize of the Society for the Study of Evolution, the Alice Hamilton Award, the Vilcek Prize for Creative Promise in Biomedical Science, the 36th Annual AACR Award for Outstanding Achievement in Cancer Research, and others. Dr. Michor’s laboratory investigates the evolutionary dynamics of cancer initiation, progression, response to therapy, and emergence of resistance.
