OVERALL - PROJECT SUMMARY The mission of the National Resource for Network Biology (nrnb.org) is to advance the science of biological networks by creating leading-edge bioinformatic methods, software tools and infrastructure, and by engaging the scientific community in a portfolio of collaboration and training opportunities. Much of biomedical research is dependent on knowledge of biological networks of multiple types and scales, including molecular interactions among genes, proteins, metabolites and drugs; cell communication systems; sample similarity networks; relationships among genotypes and biological and clinical phenotypes; and patient and social networks. NRNB technologies, like Cytoscape, are among the most widely used software tools in biology, with tens of thousands of active users, and enable researchers to analyze these networks and use them to better understand biological systems and how they are reprogrammed in disease. Since 2010, the NRNB has been supported as a Biomedical Technology Research Resource of the National Institute of General Medical Sciences. NRNB’s three technology research and development efforts seek to introduce innovative concepts with the potential to transform network biology, transitioning it from a static to a dynamic science (TR&D Project 1); from flat network diagrams to multi-scale hierarchies of biological structure and function (Project 2); and from descriptive interaction maps to interpretable and predictive network models (Project 3). In previous funding periods our technology projects have produced novel and highly cited approaches, including network-based biomarkers for stratification of disease, data-driven gene ontologies assembled completely from network data, and deep learning models of cell structure and function built using biological networks as a scaffold. NRNB has also produced widely adopted software infrastructure, including the Cytoscape ecosystem; the cBioPortal for cancer genomes and pathways; the GeneMANIA network query and gene function prediction tool; the NDEx biological networks cloud; and WikiPathways for biological pathway curation. During the next period of support, we introduce dynamic regulatory networks formulated from single-cell transcriptomics data (TR&D1); efficient algorithms for detection of hierarchical structure and pleiotropy in biological networks (TR&D2); and procedures for using networks to seed machine learning models of drug response that are both mechanistically interpretable and transferable across biomedical contexts (TR&D3). These efforts are developed and applied in close collaboration with outside investigators from 19 Driving Biomedical Projects (DBPs) who specialize in experimental generation of network data, disease biology (cancer, neuropsychiatric disorders, diabetes), single-cell developmental biology, and clinical trials. TR&Ds are also bolstered by 7 Technology Partnerships (TPs), in which NRNB scientists coordinate technology development with l...