Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations

TL;DR

PerturbODE is a novel neural ODE-based framework that models gene regulatory dynamics and infers causal networks from large-scale perturbation data, addressing limitations of previous models in expressivity and scalability.

Contribution

The paper introduces PerturbODE, a biologically informed neural ODE model that captures cell state trajectories and infers gene regulatory networks from perturbation data.

Findings

Effective in trajectory prediction and GRN inference

Works on simulated and real datasets

Addresses dynamic biological processes

Abstract

Modern high-throughput biological datasets with thousands of perturbations provide the opportunity for large-scale discovery of causal graphs that represent the regulatory interactions between genes. Differentiable causal graphical models have been proposed to infer a gene regulatory network (GRN) from large scale interventional datasets, capturing the causal gene regulatory relationships from genetic perturbations. However, existing models are limited in their expressivity and scalability while failing to address the dynamic nature of biological processes such as cellular differentiation. We propose PerturbODE, a novel framework that incorporates biologically informative neural ordinary differential equations (neural ODEs) to model cell state trajectories under perturbations and derive the causal GRN from the neural ODE's parameters. We demonstrate PerturbODE's efficacy in trajectory prediction and GRN inference across simulated and real over-expression datasets.