Presented by Dr. Kenji Kamimoto (Washington University in Saint Louis) Recent technological advances in single-cell sequencing enable the acquisition of multi-dimensional data in a high-throughput manner. These technologies reveal the existence of heterogeneity and the diversity of cell states and identities. To reveal the regulatory mechanism underlying such phenomena, many computational Gene Regulatory Network (GRN) inference methods have been proposed. However, understanding biological events from a GRN perspective remains difficult. Even if a computational algorithm can infer GRN, the biological network is so complex that it is challenging to understand how it systematically dictates cell identities. There is significant demand for new methodologies that bridge the gap between cellular phenotypes and the underlying GRN. Thus, we have developed a new method, CellOracle, a new computational approach for the inference and analysis of GRN. By utilizing machine learning algorithms and genetic information, CellOracle infers sample-specific GRN configurations from single-cell RNA-seq and ATAC-seq data. Our GRN models are designed to be used for the simulation of cell identity changes in response to gene perturbation. This simulation enables network configurations to be interrogated in relation to cell-fate regulation, facilitating their interpretation. To validate CellOracle’s GRN inference method, we present benchmarking on various tissues and cell-types. We also validate the efficacy of CellOracle to recapitulate known outcomes of well-characterized gene perturbations in developmental processes, including mouse hematopoiesis and zebrafish embryogenesis. Our benchmarking and validation results demonstrate the efficacy of CellOracle to infer and interpret the dynamics of GRN configurations, promoting new mechanistic insights into the regulation of cell identity.