Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.

ABSTRACT: Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions ("intrinsic noise") and (ii) heterogeneity of cellular states across different cells that are influenced by external factors ("extrinsic noise"). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from stochastic trajectories measured across a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experiment

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12513633 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature