Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular processes. However, causal discovery in GRNs is a challenging problem for multiple reasons including the existence of cyclic feedback loops and uncertainty that yields diverse possible causal structures. Previous works in this area either ignore cyclic dynamics (assume acyclic structure) or struggle with scalability. We introduce Swift-DynGFN as a novel framework that enhances causal structure learning in GRNs while addressing scalability concerns. Specifically, Swift-DynGFN exploits gene-wise independence to boost parallelization and to lower computational cost. Experiments on real single-cell RNA velocity and synthetic GRN datasets showcase the advancement in learning causal structure in GRNs and scalability in larger systems.

翻译：理解基因调控网络（GRNs）中的因果关系对于揭示细胞过程中的基因相互作用至关重要。然而，GRNs中的因果发现是一个具有挑战性的问题，原因包括循环反馈回路的存在以及导致多种可能因果结构的不确定性。先前在该领域的研究要么忽略了循环动力学（假设无环结构），要么在可扩展性方面存在困难。我们引入了Swift-DynGFN作为一种新颖框架，用于增强GRNs中的因果结构学习，同时解决可扩展性问题。具体而言，Swift-DynGFN利用基因独立性来提升并行化程度并降低计算成本。在真实单细胞RNA速度和合成GRNs数据集上的实验展示了在学习GRNs因果结构以及在大规模系统中的可扩展性方面的进步。