Elucidating the interaction between stretch and stiffness using an agent-based spring network model of progressive pulmonary fibrosis

Abstract

Pulmonary fibrosis is a deadly disease that involves the dysregulation of fibroblasts and myofibroblasts, which are mechanosensitive. Previous computational models have succeeded in modeling stiffness-mediated fibroblasts behaviors; however, these models have neglected to consider stretch-mediated behaviors, especially stretch-sensitive channels and the stretch-mediated release of latent TGF-β. Here, we develop and explore an agent-based model and spring network model hybrid that is capable of recapitulating both stiffness and stretch. Using the model, we evaluate the role of mechanical signaling in homeostasis and disease progression during self-healing and fibrosis, respectively. We develop the model such that there is a fibrotic threshold near which the network tends towards instability and fibrosis or below which the network tends to heal. The healing response is due to the stretch signal, whereas the fibrotic response occurs when the stiffness signal overpowers the stretch signal, creating a positive feedback loop. We also find that by changing the proportional weights of the stretch and stiffness signals, we observe heterogeneity in pathological network structure similar to that seen in human IPF tissue. The system also shows emergent behavior and bifurcations: whether the network will heal or turn fibrotic depends on the initial network organization of the damage, clearly demonstrating structure’s pivotal role in healing or fibrosis of the overall network. In summary, these results strongly suggest that the mechanical signaling present in the lungs combined with network effects contribute to both homeostasis and disease progression.

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

“…Indeed, the use of causal indices based on mathematically more sophisticated methods may provide further insights and should be the focus of future research in the field. Most importantly, because the field of network physiology is still developing with application in various diseases (Berner et al., 2022 ; dos Santos et al., 2022 ; Hall et al., 2024 ; Legault et al., 2024 ; Lehnertz et al., 2023 ; Rizzo et al., 2023 ) and scenarios including in sports and sleep analysis among others (Antonacci et al., 2023 ; Difrancesco et al., 2023 ; Ganglberger et al., 2023 ; Mangalam et al., 2024 ; Marsh et al., 2023 ; Sides et al., 2023 ), the methods available for detecting physiological connectivity as well as for quantifying such connectivity will depend on the nature of the research question being assessed, the type, quality, and quantity of data available as well as the technical repertoire at the disposal of researchers.…”