Inside Precision Medicine January 13, 2026
A research team at the University of Michigan has developed a machine-learning-based method to create digital twins of brain tumors that can estimate real-time metabolic activity and predict how individual gliomas will respond to specific treatments. The findings, published in Cell Metabolism, details the digital twins which integrate limited patient data with principles of biology, chemistry, and physics to simulate tumor metabolism, allowing clinicians to assess whether dietary interventions or metabolic drugs are likely to be effective before they are prescribed.
“Typically, metabolic measurements during surgeries to remove tumors can’t provide a clear picture of tumor metabolism—surgeons can’t observe how metabolism varies with time, and labs are limited to studying tissues after surgery,” said senior author Deepak Nagrath, PhD, a...
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