Spatial Immune Model of Alveolar Lung Infection (SIMALI) Identifies Structural Determinants of Lung Inflammation

Area of Science:

• Computational biology
• Respiratory medicine
• Pathophysiology

Background:

• Respiratory viral infections cause significant morbidity and mortality.
• Understanding lung alveolar structure's role in inflammatory damage is crucial for addressing persistent sequelae like long COVID.
• Existing computational models often lack the spatial complexity of alveolar sacs.

Purpose of the Study:

• To develop a novel computational model (SIMALI) incorporating alveolar structure to simulate viral infection and inflammation.
• To investigate how lung architecture influences the spatial-temporal spread of infection and inflammatory damage.
• To validate the model against patient CT scan data of SARS-CoV-2 infection.

Main Methods:

• Developed SIMALI, a 3D lattice model of alveolar sacs with air, epithelial cells, and surrounding tissue.
• Incorporated physiological percentages of infectable cells and differential diffusion of virus.
• Validated simulation predictions against spatial-temporal lung lesion growth from patient CT scans.

Main Results:

• SIMALI accurately predicts the growth of lung inflammation observed in SARS-CoV-2 patient CT scans.
• The model demonstrates that alveolar architecture and cell distribution constrain viral and inflammatory spread.
• Initial viral deposition patterns significantly impact the variation in inflammatory lung damage.

Conclusions:

• Lung alveolar structure plays a key role in modulating inflammatory responses to viral infections.
• Variability in patient lung damage is influenced by alveolar structure, immune response, and initial viral deposition.
• SIMALI provides a framework for understanding the mechanisms behind diverse patient outcomes in respiratory viral infections.