发表机构
Yanbian University; State University of New York at Buffalo（延边大学; 纽约州立大学布法罗分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将从单细胞RNA速度解码基因调控网络的问题转化为稀疏动力学逆问题，证明受控扰动可恢复可识别性，提出积分重建方法并建立有限样本保证，为相关研究提供数学基础。
AI 中文摘要
单细胞RNA测序提供细胞状态的快照，而RNA速度提供其时间演化的方向信息。一个基本问题是，能否从这类不完整观测中恢复出调控细胞动态的隐藏基因调控网络。本研究中，我们通过将该问题表述为耦合转录-剪接动态产生的稀疏动力学逆问题，开发了一种从RNA速度数据重建调控网络的数学框架。我们表明，被动快照观测通常不足以唯一识别调控相互作用，揭示了未受扰动测量的固有局限性。随后，我们证明受控扰动通过生成信息丰富的调控轨迹恢复了可识别性，并表征了唯一恢复的条件。为应对有噪声和不完美的观测，我们引入了一种避免数值微分的积分重建方法，并为稀疏网络恢复建立了有限样本保证。我们的结果为理解何时以及如何从单细胞观测中解码基因调控网络提供了数学基础，连接了扰动设计、逆问题理论和细胞反应的预测建模。
英文摘要
We formulate gene regulatory network reconstruction from RNA velocity as a sparse dynamical inverse problem. We show that control-only data can be structurally nonidentifying and characterize excitation conditions under which controlled perturbations restore identifiability by generating complementary regulator trajectories. To enable stable reconstruction from noisy data, we develop an integral sparse estimator that avoids numerical differentiation and derive recovery bounds separating stochastic error from systematic contributions due to latent-time uncertainty, kinetic-parameter error, numerical quadrature, and model misspecification. Synthetic experiments illustrate perturbation-assisted identifiability, improved conditioning, and the robustness of integral reconstruction. Applied to perturbation-resolved RPE1 RNA-velocity data, the framework yields an empirically full-rank design whose conditioning improves with perturbational diversity and a reconstructed network core stable under perturbation subsampling. Held-out evaluation further shows that identifiability and reconstruction stability do not imply uniform predictive improvement. These results connect perturbational excitation, identifiability, and stable sparse recovery in regulatory dynamical systems.
Comments24 pages, 5 figures
