Temporal AI model predicts drivers of cell state trajectories across human aging

Gomez Ortega, J.; Nadadur, R. D.; Kunitomi, A.; Kothen-Hill, S.; Wagner, J. U. G.; Kurtoglu, S. D.; Kim, B.; Reid, M. M.; Lu, T.; Washizu, K.; Zanders, L.; Chen, H.; Zhang, Y.; Ancheta, S.; Lichtarge, S.; Johnson, W. A.; Thompson, C.; Phan, D. M.; Combes, A. J.; Yang, A. C.; Tadimeti, N.; Dimmeler, S.; Yamanaka, S.; Alexanian, M.; Theodoris, C. V.

doi:10.64898/2026.03.30.715396

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bioRxiv · 10.64898/2026.03.30.715396

Temporal AI model predicts drivers of cell state trajectories across human aging

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Abstract

Foundational AI models have recently shown promise for predicting the impact of perturbations on cell states. However, current models typically consider only one cell state at a time, limiting their ability to learn how cellular responses unfold over time, particularly across long trajectories such as diseases of aging. Here, we develop a temporal AI model, MaxToki, trained on nearly 1 trillion gene tokens including cell state trajectories across the human lifespan to generate cell states across long timelapses of human aging. MaxToki generalized to unseen trajectories through in-context learning and predicted novel age-modulating targets that were experimentally verified to influence age-related gene programs and functional decline in vivo. MaxToki represents a promising strategy for temporal modeling to accelerate the discovery of interventions for programming therapeutic cellular trajectories.

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Gomez Ortega, J., Nadadur, R. D., Kunitomi, A., Kothen-Hill, S., Wagner, J. U. G., Kurtoglu, S. D., Kim, B., Reid, M. M., Lu, T., Washizu, K., Zanders, L., Chen, H., Zhang, Y., Ancheta, S., Lichtarge, S., Johnson, W. A., Thompson, C., Phan, D. M., Combes, A. J., Yang, A. C., Tadimeti, N., Dimmeler, S., Yamanaka, S., Alexanian, M., Theodoris, C. V.. 2026-04-01. Temporal AI model predicts drivers of cell state trajectories across human aging. https://doi.org/10.64898/2026.03.30.715396

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