Dissecting Cell Identity via Network Inference and In Silico Gene Perturbation

Explore cutting-edge methods for defining and controlling cell identity in this comprehensive seminar from the Broad Institute's Models, Inference and Algorithms series. Delve into CellOracle, a novel machine learning-based tool that leverages single-cell multi-omics data to investigate how transcription factors regulate cell identity. Learn about the conceptual framework of CellOracle and its ability to predict cell identity shifts following in silico transcription factor perturbations. Examine applications in mouse and human hematopoiesis, zebrafish embryogenesis, and cellular reprogramming. Discover how systematic in silico perturbation of transcription factors in developing zebrafish led to new insights into axial mesoderm formation. Gain understanding of CellOracle's predictive capabilities, validated against known biological phenomena, as well as its limitations. Presented by Samantha Morris and Kenji Kamimoto from Washington University in St. Louis, this talk offers valuable insights for researchers interested in computational biology, developmental biology, and gene regulation.