Dr. Neda Bagheri publishes article in PNAS

Discovery of gene regulatory networks (GRNs) is crucial for gaining insights into biological processes involved in development or disease. Investigating the temporal dynamics of regulatory networks is particularly insightful. We introduce Sliding Window Inference for Network Generation (SWING), which uniquely accounts for temporal information underlying GRNs, such as protein synthesis and posttranslational modifications. We validate SWING in both in silico and in vitro experimental systems, highlighting improved performance in identifying time-delayed edges and illuminating network structure. SWING performance is robust to user-defined parameters, enabling identification of regulatory mechanisms from time-series gene expression data.