The U.S. government, Meta, Google DeepMind, and other partners are combining nearly $1.8 billion in funding and existing resources to build open biological datasets for models designed to predict how living cells behave.

The effort expands Biohub’s Virtual Biology Initiative, which aims to develop “virtual cells” that researchers could eventually use to predict cellular responses and conduct some biological experiments digitally. Biohub is a nonprofit research organization focused on combining computational tools with biology.

Google DeepMind, drug-discovery company Isomorphic Labs and Meta are collectively investing $300 million in the initiative. The U.S. Department of Energy will contribute more than $500 million over five years for biological measurements, modeling and computing.

The National Institutes of Health will coordinate datasets, repositories and other resources developed through more than $500 million in previous federal investment. Biohub will work with NIH to standardize the information so it can be used to train predictive models.

The project addresses a major obstacle to applying advanced computational models to biology: researchers need large amounts of standardized experimental data showing how cells behave and respond when conditions change.

Biohub says the resulting datasets will be openly available to researchers. Rather than simply cataloging cells, the goal is to provide enough measurements for models to predict how a cell will respond to an intervention, potentially helping researchers investigate diseases and treatments before moving to laboratory experiments.

“An accurate predictive model of biology could dramatically accelerate scientific discovery by enabling scientists to perform experiments digitally,” said Biohub Head of Science Alex Rives.

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