Predictive Modeling of Gene Regulatory Networks

Abstract

Gene regulatory networks govern the spatiotemporal expression programmes that determine cell identity, differentiation, and disease, and predictive modelling of these networks from high-throughput data is essential for understanding cellular decision-making, yet no consensus exists on which computational framework best balances predictive accuracy with biological interpretability. We evaluated 210 GRN predictive modelling programmes across centres affiliated with the Swiss Institute of Machine Intelligence in Zurich between 2016 and 2021, spanning five framework categories: Boolean and logic-based network models, ordinary differential equation kinetic models, Bayesian network structure learning, random forest and gradient boosting regression, and deep-learning-based regulatory inference. A Gene Regulatory Network Modelling Index (GRNMI) was constructed from five sub-scores -- regulatory edge prediction accuracy, expression dynamics prediction, perturbation response prediction, scalability to genome-wide networks, and experimental validation rate -- with weights from regression against sustained adoption into active regulatory genomics pipelines. GRNMI correlated with adoption at r = +0.83 and discriminated adopted from non-adopted frameworks with an AUC of 0.880. Random forest and gradient boosting methods scored highest (mean GRNMI 0.818), while Boolean models trailed at 0.594. Only 34.3 percent of programmes exceeded the 0.75 threshold. Edge prediction accuracy carried the largest regression weight (beta = +0.280), followed by perturbation response prediction (beta = +0.228).