Perspectives on physics-based one-dimensional modeling of lung physiology

Area of Science:

• Pulmonary Medicine
• Mathematical Modeling
• Infectious Disease Dynamics

Background:

• The SARS-CoV-2 pandemic highlighted the need to understand deep lung infection spread.
• Modeling aerosol transport, mucus clearance, and infection kinetics in the airways presents significant challenges.

Purpose of the Study:

• To consolidate the view of coupled one-dimensional (1D) physics-based mathematical models for lung physiology.
• To review the successes and limitations of 1D trumpet models in understanding lung function.
• To explore the potential of physics-based modeling for clinical applications in respiratory diseases.

Main Methods:

• Review of existing 1D physics-based mathematical models for lung physiology.
• Analysis of non-dimensional numbers governing lung function and disease parameters.
• Conceptualization of a 'virtual disease landscape' using a physics-based parameter space.

Main Results:

• 1D models provide mechanistic insights into lung function and optimalities.
• Key non-dimensional numbers defining lung function have been identified.
• A framework for mapping pathologies onto a non-dimensional parameter space is proposed.

Conclusions:

• 1D modeling is a valuable tool for understanding lung physiology and disease.
• Mapping pathologies onto a physics-based parameter space can aid diagnosis and personalized treatment.
• Future directions include translating 1D modeling to point-of-care clinical applications.