raunakdey / metapop_lung

(Review) Repository for the metapopulation model of a P.a. lung infection

Code for: Metapopulation model of phage therapy of an acute Pseudomonas aeruginosa lung infection

Abstract: Multi-drug resistant (MDR) infections caused by pathogenic bacteria are a global health threat. Phage therapy, or the use of phage to kill bacterial pathogens infecting a host, constitutes a potential alternative approach to treating MDR infections. However, the therapeutic outcome of phage may be limited by the emergence of phage resistance during treatment and/or by physical constraints that impede phage-bacteria interactions in vivo. In this work, we evaluate the lung spatial structure effects on the phage therapy of a Pseudomonas aeruginosa (P.a.) infection. To do so, we developed a spatially structured metapopulation network model based on the geometry of the bronchial tree, including the potential for emergence of phage-resistant bacterial mutants. We model the ecological interactions between bacteria, phage, and the host innate immune system at the airway (node) level. The model predicts the synergistic elimination of a P.a. infection due to the combined effects of phage and neutrophils given sufficiently active immune states and suitable phage life history traits. Moreover, via a combination of metapopulation simulations and theory incorporating finite volume effects, we predict that local MDR pathogens are cleared faster at distal nodes of the bronchial tree. Notably, image analysis of lung tissue time series from wild-type and innate lymphocyte depleted mice (n=13) revealed a concordant, statistically significant pattern: infection intensity cleared in the bottom before the top of the lungs. Overall, the combined use of theory, simulations, and image analysis of in vivo experiments further supports the use of phage therapy for treating acute lung infections caused by P.a. while highlighting potential limits to therapy given the spatial structure inherent in such infections.

Code usage

All the scripts used to generate main and supplementary figures are written in MATLAB (R2020b).

1. Run the script 'Generate_all_figs.m' outside any folders to generate main and supplementary figures.
   1. In case you require more Java Heap Memory to run the 'Generate_all_figs.m' script, go to MATLAB -> Preferences -> General -> Java Heap memory, and increase the Java heap size, then click on apply and okay.

• metapop_code: directory contains all the scripts (.m) and functions necessary to generate metapopulation model simulations
• image_analysis: directory contains all the scripts (.m) and functions necessary to carry out imaging analysis of P.a. infected mice
• figures: directory with saved figures after running Generate_all_figs.m
• data: directory with necessary data to run the metapopulation model scripts

Note: I have pre-saved the data obtained after performing the robustness analysis of the metapopulation model inside the data folder. Hence, the scripts that generate the figures from the robustness analysis used the pre-saved data. To generate the data from scratch (it takes 13 hr per code), uncomment lines 45 to 73 of the script 'Fig5_heatmap_adsorption_rate_nlung.m' and lines 40 to 69 of the script 'FigS3_heatmap_mucin_nlung.m' both scripts are inside the metapop_code folder.