Assess graduate-level competence in critically evaluating statistical and computational approaches for inferring gene regulatory networks (GRNs) from single-cell RNA-seq data, including preprocessing choices (normalization, batch correction, imputation), model classes (correlation, mutual information, regression, Bayesian networks, tree- and ensemble-based methods, dynamical and pseudotime-based models), algorithmic assumptions, scalability and complexity, hyperparameter selection, benchmarking strategies (simulations, gold-standards, perturbation/perturb-seq), evaluation metrics (precision-recall, AUROC, F1, calibration), interpretability and causal claims, and experimental validation; students should compare strengths/limitations, design fair benchmarks, and justify method selection for specific data types and biological questions.