The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.
Bibliographical noteFunding Information:
This research was supported by NIH grants: U01 HL-124052 , Awarded to J.H.T.B., R01AI123093 and U01 HL131072 awarded to D.E.K., HL144481 awarded to B.B.M., K23HL143135 awarded to C.A.B., HL127283-05 awarded to T.H.B. Support from UVA Pinn Scholars to S.M.P and the UVA Fibrosis Initiative awarded to T.H.B. The authors would like to thank Susan Leonard-Duke for her contributions to Fig. 1 .
© 2020 Elsevier B.V.
- Lung fibrosis
- Multi-scale computational models