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.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.