Aviv Regev on AI, rich datasets and the path to a 'virtual cell'

Summary

Aviv Regev is a computational biologist who leads Genentech Research and Early Development. Previously a professor at the Massachusetts Institute of Technology and a leader at the Broad Institute of M.I.T. and Harvard University, her research has advanced single-cell genomics, systems biology and large-scale efforts to map human cells. [This interview was edited for length and clarity.] How would you describe the current state of American science? On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. We are at an exciting inflection point in the history of health care. The combination of artificial intelligence, data, large-scale lab experiments and the ability to measure human biology are driving unprecedented insights and innovation in biology, drug discovery and medicine. We now have the experimental capabilities to gather very large, high-resolution, rich datasets and build deeply informative resources to drive biomedicine forward. Those rich datasets are ideally deeply integrated with AI, which can help analyze the data and fill in the gaps in what it is possible to capture experimentally and can also guide the iterative generation of additional data to gradually get to better and better models. For example, single-cell genomics allows us to understand the molecular composition and classification of individual cells, which we can then use to build a reference such as the Human Cell Atlas. As we gather more and more data about cells—the basic unit of life—we can use that to build a “virtual cell” model of how cells behave, and understand how they might behave differently in the context of disease or therapy. At the same time, we can use AI to power every stage of drug discovery, from the early stages as I just described of understanding the biology to developing the ideal medicines to target the pathways underlying disease and assessing their safety. What needs to change in American science? Basic research into foundational scientific problems is critical for innovation, and sufficient stability is important for open-ended innovation. Scientists are less likely to take risks if the environment is unstable, and science thrives in setting…