Understanding causal relationships among biomolecules is essential for deciphering biological systems and disease mechanisms. While traditional protein–protein interaction networks provide valuable insights into functional associations, they are often limited by their undirected nature, which restricts their ability to support causal reasoning and dynamic modeling. Recent advances in regulatory interaction databases have enabled the construction of directed regulatory networks that capture activation, inhibition, and signaling relationships among genes and proteins. In this study, we propose a computational framework that integrates directed regulatory networks with causal inference algorithms and dynamic systems modeling. The framework combines literature-derived regulatory interactions with probabilistic graphical modeling and differential equation-based simulations to identify key regulatory drivers and predict system behavior under perturbations. Experimental evaluation using benchmark biological pathways demonstrates that incorporating directional information significantly improves causal discovery and predictive modeling accuracy. Our results highlight the importance of directed regulatory networks for systems biology and suggest that integrating causal inference with dynamic modeling can enhance the
