LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data

Deciphering gene regulatory mechanisms from high-dimensional biology data remains a central challenge in modern systems biology, despite the growing availability of single-cell datasets. The difficulty stems partly from the sparsity and noise inherent in single-cell data and partly from the complexity of dynamic combinatorial regulation mediated by transcription factors. In this work, we introduce LogicSR, a computational framework that reconstructs gene regulatory networks from single-cell gene expression data with high accuracy by integrating the mechanistic interpretability of Boolean logical models with the equation-discovery capabilities of symbolic regression. It incorporates prior knowledge into a multi-objective Monte Carlo tree search (MCTS) framework, leveraging it to ensure biological plausibility and accelerate the search for optimal governing equations. LogicSR outperforms existing methods on both synthetic and real-world benchmark datasets. When applied to a human embryonic stem cell dataset, it demonstrates superior performance in elucidating complex combinatorial TF-target gene regulations and identifying key regulators.