An international public-private consortium led by Biohub, the U.S. Department of Energy, and the National Institutes of Health is committing $1.8 billion to build an open, AI-ready biological data resource to enable predictive models of cells and disease. Contributions include Biohub’s $500 million anchor (with $400 million for new measurement technologies such as cryo-electron tomography and high-throughput microscopy and $100 million for external research), DOE’s Genesis Mission providing over $500 million across five years for exascale computing, advanced imaging and autonomous labs, NIH coordinating more than $500 million of existing biomedical datasets and infrastructure, and $300 million from Google DeepMind, Isomorphic Labs, and Meta. Major research institutes, NVIDIA, and philanthropic partners will support standards, computing, and funding expansion.
The effort will generate and standardize multimodal datasets, expand measurements of cell responses to interventions across many cell types and conditions, and deliver shared standards, common identifiers, and a single point of access to accelerate model training and validation. By combining large-scale measurement, imaging, and computation - building on resources like Tabula Sapiens and CryoET portals - the program intends to enable in‑silico experiments, shorten timelines for therapeutic discovery, and coordinate a global scientific community to construct validated “virtual cell” models that predict biological responses and guide disease prevention and treatment.
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