Modern high-throughput biological datasets containing thousands of perturbations enable large-scale discovery of causal graphs that represent regulatory interactions between genes. Differentiable causal graphical models and regression-based methods have been developed to infer gene regulatory networks (GRNs) from interventional datasets. However, existing approaches fail to capture the non-linear dynamics of biological processes such as cellular differentiation. To address this limitation, we propose PerturbODE, a novel framework that employs interpretable neural ordinary differential equations (neural ODEs) to model cell state trajectories under perturbations and derive the underlying causal GRN from the neural ODE parameters, enabling downstream simulation of unseen genetic interventions. The GRN is encoded via a single-hidden-layer feedforward network, implicitly grouping genes into interpretable co-regulated modules. We demonstrate PerturbODE's efficacy in GRN inference and extension to perturbation response prediction across both simulated and real overexpression datasets.

翻译：现代高通量生物数据集包含数千种扰动，使得大规模发现代表基因间调控相互作用的因果图成为可能。可微分因果图模型和基于回归的方法已被开发用于从干预数据集中推断基因调控网络（GRN）。然而，现有方法未能捕捉生物过程（如细胞分化）的非线性动态。为解决这一局限性，我们提出PerturbODE框架，该框架采用可解释神经常微分方程对扰动下的细胞状态轨迹进行建模，并从神经ODE参数中推导出潜在的因果GRN，从而实现对未知遗传干预的下游模拟。GRN通过单隐藏层前馈网络编码，隐式地将基因分组为可解释的共调控模块。我们在模拟和真实过表达数据集上展示了PerturbODE在GRN推断及扰动响应预测扩展方面的有效性。