An imaging-based computational framework for simulation of airflow in subject specific breathing human lungs is established. The three-dimensional (3D) airways of up to 9 generations and lobes are segmented and reconstructed from computed tomography (CT) images. Beyond the CT-resolved 3D airways, a volume filling method is applied to generate the one-dimensional (1D) conducting airway tree that bridges the central airway with the lung parenchyma. Through 3D-1D airway coupling, a novel image-registration-based boundary condition (BC) is proposed to derive physiologically-consistent regional ventilation for the whole lung and provide flow-rate fractions needed for the 3D airway model via the 1D-tree connectivity and the mass conservation. The in-house parallel finite-element large-eddy simulation (LES) code enables to capture genuinely complex airflow characteristics in a computationally-efficient manner. The 3D-1D coupling framework is multiscale because it can not only predict detailed flows in the 3D central airways at a local level, but also yields subject-specific physiologically-consistent regional ventilation at the whole lung level. The framework has been applied to investigate pulmonary airflow and lung physiology. For example, the study of intra- and inter-subject variability provides insight into the effect of airway geometry on airflow structure. The relations between airflow structure, energy dissipation, and airway resistance under normal breathing condition have also been studied, showing similarity behaviors for inspiratory and expiratory flows. In the study of high-frequency oscillatory ventilation, we have compared counter-flow structures near flow reversal (namely phase change between inspiration and expiration) and quantified associated convective mixing in both idealized and CT-based airway models. Furthermore, the image-registration-derived displacement field is used to deform 3D-1D airway models for breathing lung simulation and estimate diameter changes of 1D airway segments during deformation. In conjunction with an arbitrary Lagrangian Eulerian method, airflow in a breathing lung has been simulated and compared with that of a rigid airway model. The results show that the proposed computational framework is promising in better understanding the human lung physiology and improving the treatment of diseased lung.