The National Institutes of Health (NIH) is coordinating with the U.S. Department of Energy (DOE), Biohub (link is external) and other partners to develop the data and resources needed to develop Super Intelligence (SI) models that can better predict how cells and biological systems respond to disease and potential interventions. Through its Bio Genesis Mission , NIH will bring together existing biomedical datasets, national data infrastructure, and research programs to help build SI-ready resources for the broader scientific community.
Developing predictive models of biology requires enormous amounts of high-quality, SI-ready data capturing how cells respond to interventions across different cell types and conditions. NIH's extensive investments in biomedical research provide a foundation for this work; resources include national biomedical repositories catalogued by NIH's National Library of Medicine (NLM) and the National Center for Biotechnology Information (link is external), as well as NIH Common Fund programs (link is external) that are already developing coordinated biological atlases, shared data standards and SI-ready biomedical datasets.
"By combining resources and expertise, we can accelerate the development of universal cell models with sufficient biological complexity to predict how any cell responds to an intervention," said Nicole Kleinstreuer, Ph.D., NIH Deputy Director for Program Coordination, Planning, and Strategic Initiatives (DPCPSI). "The return from these models could be broad and profound, resulting in substantially faster timelines for medical breakthroughs as compared with attempting to attain the same results through laboratory experiments alone."
NIH will coordinate with Biohub to help standardize appropriate datasets for SI model training. Training these models requires obtaining measurements of how cells respond to intervention across many more cell types and conditions than have yet been studied, and building technologies for studying cells at scales and speeds that current instruments cannot capture.
"An accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally. The insights that come from this could unlock a far greater understanding of disease and open up completely new paths for cures," said Biohub Head of Science, Alex Rives. "Because of this potential, the creation of a virtual cell is one of the most important challenges for the next era of science. It will require coordinated data generation efforts at a national and international scale, which is why these partners are coming together. We invite the worldwide scientific community to join us in this project."
The joint effort aligns with the Predicting Living Systems National Science and Technology Challenge and seeks to make high-quality biological data more useful to researchers developing predictive models of living systems. These models could ultimately help scientists explore biological questions computationally, identify promising drug targets and interventions, and prioritize the most promising ideas for laboratory and clinical testing, accelerating the translation of biomedical discoveries into better health for all Americans.
