Coarse-graining genome-scale metabolic models for dynamic, multi-scale simulations of Pseudomonas aeruginosa in gel environments

GEM-CM couples reaction–diffusion with a coarse-grained GEM surrogate to predict Pseudomonas aeruginosa PA14 growth and pyocyanin spatial dynamics in low weight percentage agar gels.

• Why it matters: Parameterizing links between intracellular metabolism and extracellular microenvironments is a barrier for adapting multi-scale simulations across in vitro systems, limiting mechanistic interpretation of infection-relevant heterogeneity.
• What they did: The framework uses random forest regression to coarse-grain the GEM–reaction–diffusion interface via feature-importance-selected metabolites, then predicts metabolic rates from flux balance analysis outputs. It was evaluated against PA14 radial propagation in agar gels using three carbon-source inputs and tested under bacterial motility changes and single gene knockout conditions.
• The result: GEM-CM captured PA14 biomass distributions over time and reproduced partial or total biomass and pyocyanin inhibition seen in transposon mutant screenings, and it suggested metH as a promising target to inhibit spatial propagation.

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