{"id":31230,"date":"2023-09-19T07:45:00","date_gmt":"2023-09-19T11:45:00","guid":{"rendered":"https:\/\/platohealth.ai\/how-ai-can-help-us-understand-how-cells-work-and-help-cure-diseases\/"},"modified":"2023-09-19T12:18:37","modified_gmt":"2023-09-19T16:18:37","slug":"how-ai-can-help-us-understand-how-cells-work-and-help-cure-diseases","status":"publish","type":"post","link":"https:\/\/platohealth.ai\/how-ai-can-help-us-understand-how-cells-work-and-help-cure-diseases\/","title":{"rendered":"How AI can help us understand how cells work\u2014and help cure diseases","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
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As the smallest living units, cells are key to understanding disease\u2014and yet so much about them remains unknown. We do not know, for example, how billions of biomolecules\u2014like DNA, proteins, and lipids\u2014come together to act as one cell. Nor do we know how our many types of cells interact within our bodies. We have limited understanding of how cells, tissues, and organs become diseased and what it takes for them to be healthy.<\/p>\n

AI can help us answer these questions and apply that knowledge to improve health and well-being worldwide\u2014if researchers can access and harness these powerful new technologies. <\/p>\n<\/p><\/div>\n<\/div>\n

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Imagine if we had a way to represent every cell state and cell type using AI models. A \u201cvirtual cell\u201d could simulate the appearance and known characteristics of any cell type in our body\u2014from the rods and cones that detect light in our retinas to the cardiomyocytes that keep our hearts beating.<\/p>\n

Scientists could use such a simulator to predict how cells might respond to specific conditions and stimuli: how an immune cell responds to an infection, what happens at the cellular level when a child is born with a rare disease, or even how a patient\u2019s body will respond to a new medication. Scientific discovery, patient diagnosis, and treatment decisions would all become faster, safer, and more efficient.<\/p>\n

At the Chan Zuckerberg Initiative, we\u2019re helping to generate the scientific data and build out the computing infrastructure to make this a reality\u2014and give scientists the tools they need to take advantage of new advances in AI to help end disease.<\/p>\n

The data<\/h3>\n

Advances in AI coupled with large volumes of scientific data have already predicted the structure of nearly all known proteins. DeepMind trained AlphaFold<\/a> on 50 years\u2019 worth of carefully collected data, and in just five years, they solved the mystery of protein structure. ESM<\/a>, another AI system which was developed at Meta, is a protein language model trained not on words but on over 60 million protein sequences. It is used for a wide range of applications, like predicting protein structures and the effects of mutations from single sequences.<\/p>\n

A virtual cell modeling system will also require large amounts of data. Since 2016, CZI has supported researchers globally in efforts to generate and annotate data about cells and their components, built tools to integrate these large data sets, and made them widely available for researchers to learn from and build upon.<\/p>\n

A global consortium of researchers has been building a reference map of every cell type in the body, and our San Francisco Biohub is creating whole-organism cell atlases<\/a>. Together, these data sets are yielding the first draft of the open-source Human Cell Atlas<\/a>, which will chart cell types in the body from development to adulthood. Our SF Biohub and the Chan Zuckerberg Imaging Institute<\/a> are partnering on OpenCell<\/a>, which maps the locations of different proteins in our cells.<\/p>\n<\/p><\/div>\n<\/div>\n