The mission of the National Resource for Network Biology (NRNB) 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; relationships among genotypes and biological and clinical phenotypes; and patient and social networks. NRNB-supported platforms like Cytoscape are among the most widely used software tools in biology, with tens of thousands of active users, enabling researchers to apply network concepts and data to understand biological systems and how they are reprogrammed in disease. NRNB?s three Technology Research and Development projects introduce innovative concepts with the potential to transform network biology, transitioning it from a static to a dynamic science (TR&D 1); from flat network diagrams to multi-scale hierarchies of biological structure and function (TR&D 2); and from descriptive interaction maps to predictive and interpretable machine learning models (TR&D 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. During the next period of support, we introduce dynamic regulatory networks formulated from single-cell transcriptomics and advanced proteomics data (TR&D 1); substantially improved methodology for the study of hierarchical structure and pleiotropy in biological networks (TR&D 2); and procedures for using networks to seed machine learning models of drug response that are both mechanistically interpretable and transferable across biomedical contexts (TR&D 3). These efforts are developed and applied in close collaboration with outside investigators from 19 Driving Biomedical Projects 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 in which NRNB scientists coordinate technology development with leading resource-development groups in gene function prediction, mathematics and algorithm development, and biomedical databases. Beyond these driving collaborations, we continually support a broader portfolio of transient (non-driving) research collaborations; organize and lead international meetings including the popular Network Biology track of the Intelligent Systems for Molecular Biology conference; and deliver a rich set of training opportunities and network analysis protocols.
We are all familiar with some of the components of biological systems ? DNA, proteins, cells, organs, individuals ? but understanding biological systems involves more than just cataloging its component parts. It is critical to understand the many interactions of these parts within systems, and how these systems give rise to biological functions and responses and determine states of health and disease. The National Resource for Network Biology provides the scientific community with a broad platform of computational tools for the study of biological networks and for incorporating network knowledge in biomedical research.
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