Author

Praful Gagrani

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Preprint Sep 2026

Large-deviations theory for growing chemical reaction networks

Growing biochemical systems are intrinsically noisy, with one important source of stochasticity arising from the discrete reaction events of the underlying biochemical network. Because growing systems do not generally admit stationary abundance distributions, it is unclear how to separate this intrinsic chemical noise...

Praful Gagrani, Ignacio Madrid, Tetsuya J. Kobayashi · 0 citations

#machine learning Preprint Sep 2026

CRNDiff: Count-Native Diffusion Framework via Chemical Reaction Networks

CRNDiff, a structured framework that combines count-space diffusion with inference-time conditioning on rare subpopulations, and tilted Feynman--Kac steering, a method for sampling target subpopulations from a frozen generator without retraining are introduced.

Yu-Shan Qiu, Praful Gagrani, Tetsuya J. Kobayashi · 0 citations