In this paper, we present a dynamic patient-specific model of the respiratory system for a whole respiratory cycle, based on 4D CT scans, personalized physiological compliance (pressure-volume curves), as well as an automatic tuning algorithm to determine lung pressure and diaphragm force parameters. The amplitude of the lung pressure and diaphragm forces are specific, and differs from one patient to another and depends on geometrical and physiological characteristics of the patient. To determine these parameters at different respiratory states and for each patient, an inverse finite element (FE) analysis has been implemented to match the experimental data issued directly from 4D CT images, to the FE simulation results, by minimizing the lungs volume variations. We have evaluated the model accuracy on five selected patients, from DIR-Lab Dataset, with small and large breathing amplitudes, by comparing the FE simulation results on 75 landmarks, at end inspiration (EI), end expiration (EE) states, and at each intermediate respiratory state. We have also evaluated the tumor motion identified in 4D CT scan images and compared it with the trajectory obtained by FE simulation, during one complete breathing cycle. The results demonstrate the good quantitative results of our physic-based model and we believe that our model, despite of others takes into account the challenging problem of the respiratory variabilities.