Reinforced concrete bridges are predominant structural systems in transportation infrastructure. Their exposure to chronic and sudden stressors, such as corrosion and earthquakes, make them prone to risks with severe socioeconomic consequences. While time-dependent single-component seismic fragility formulations have advanced the frontier of life-cycle probabilistic risk assessment, state-dependent multi-component representations of damage and deterioration, paramount for structural integrity management, still lack a systematic probabilistic framework. This paper develops a novel dynamic Bayesian network to evaluate the life-cycle fragility functions of aging bridges, encapsulating the impacts of corrosion and seismic phenomena over time. The network establishes Markovian transitions among deterioration states for various bridge components integrating chloride diffusion and corrosion propagation models with non-stationary Gamma processes. A methodology for deriving and state-dependent fragility at the component and system levels depending on several deterioration scenarios is presented. Our framework is exemplified in an archetypical 4-span bridge, demonstrating the longitudinal effects of corrosion on the system's seismic fragility for splash and atmospheric conditions. Insights from the multi-component analysis highlight the capabilities in understanding the pathologies and evolving mechanical interactions among components. The adaptability in accommodating on-site observations and advanced decision-making algorithms is discussed, demonstrating the suitability of the framework for applications requiring flexible and updatable virtual environments.