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.

Gladstone Institutes; University of California, San FranciscoGladstone Institutes; University of California, San Francisco · biorxiv

Paper

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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Research summary

The paper claims that the MaxToki model can predict novel age-modulating targets that influence age-related gene programs and functional decline. This research is relevant as it addresses the underlying mechanisms of aging and seeks to identify interventions that could potentially alter cellular trajectories associated with aging.

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