Abstract
The current study aims to investigate the relative contributions of each of the processes that govern airway smooth muscle mechanical behaviour. Studies have shown that breathing dynamics have a substantial effect on airway constriction in healthy and diseased subjects, yet little is known about the dynamic response of the main instigator of airway constriction, Airway Smooth Muscle (ASM). In this work several models are developed to further the understanding of ASM dynamics, particularly the roles and interactions of the three dominant processes in the muscle: contractile dynamics, length adaptation and passive dynamics. Three individual models have been developed, each describing a distinct process or structure within the muscle. The first is a contractile model which describes the contractile process and the influence of external excitation on contractile behaviour. The second model incorporates the contractile model to describe length adaptation, which includes the reorganisation and polymerisation of contractile elements in response to length changes. The third model describes the passive behaviour of the muscle, which entails the mechanical behaviour of all non-contractile components and processes. As little data on the passive dynamics of the muscle was available in the literature, a number of experiments were conducted to investigate relaxed ASM dynamics. The experimental data and mathematical modelling showed that passive dynamics plays not only a dominant role in relaxed ASM, but contributes considerably to the dynamics of contracted muscle as well. A novel theory of sequential multiplication in passive ASM is proposed and implemented in a mathematical model. Experiments and literature validated the model simulations. Further integration of the models and improved force control modelling of length adaptation is proposed for future study. It is likely that the coupling of the models presented here with models describing other airway wall components will provide a more complete picture of airway dynamics, which will be invaluable for understanding respiratory disease.
