The seemingly simple process of inhalation relies on a complex interplay between muscular contraction in the thorax, elastocapillary interactions in individual lung branches, propagation of air between different connected branches, and overall air flow into the lungs. These processes occur over considerably different length and timescales; consequently, linking them to the biomechanical properties of the lungs, and quantifying how they together control the spatiotemporal features of inhalation, remains a challenge. We address this challenge by developing a computational model of the lungs as a hierarchical, branched network of connected liquid-lined flexible cylinders coupled to a viscoelastic thoracic cavity. Each branch opens at a rate and a pressure that is determined by input biomechanical parameters, enabling us to test the influence of changes in the mechanical properties of lung tissues and secretions on inhalation dynamics. By summing the dynamics of all the branches, we quantify the evolution of overall lung pressure and volume during inhalation, reproducing the shape of measured breathing curves. Using this model, we demonstrate how changes in lung muscle contraction, mucus viscosity, and surface tension, and airway wall stiffness - characteristic of many respiratory diseases, including those arising from COVID-19, cystic fibrosis, chronic obstructive pulmonary disease, asthma, and emphysema - drastically alter inhaled lung capacity and breathing duration. Our work, therefore, helps to identify the key factors that control breathing dynamics and provides a way to quantify how disease-induced changes in these factors lead to respiratory distress.
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)