Network structures and parameters in multiscale modeling in ErbB signaling networks

Signal transduction is a complex system governing cellular behavior across physiological and pathological contexts. Advances in systems biology have positioned cell modeling as a powerful tool for reconstructing the dynamics and trajectories of disease processes. Nevertheless, despite progress in AI-assisted model generation, parameter estimation remains a challenge, especially under data constraints. In contrast, molecular dynamics simulations offer crucial, high-resolution insights by uncovering conformational activation mechanisms and by extracting kinetic parameters; however, they face scalability limitations. This review focuses on modeling of the ErbB signaling system, highlighting recent advances at both the cellular and molecular scales. Emerging trends, such as simulation data reuse, machine learning-guided network inference, and modeling within realistic environmental contexts, are now driving a compelling integration of these molecular and cellular modeling paradigms.