Spatial transcriptome-guided multi-scale framework connects < i > P < /i > . < i > aeruginosa < /i > metabolic states to oxidative stress biofilm microenvironment

by Tracy J. Kuper, Mohammad Mazharul Islam, Shayn M. Peirce-Cottler, Jason A. Papin, Roseanne M Ford With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulatePseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration ofP.aeruginosa PA14 biofilm spatial transcriptomic data into aP.aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent ’s local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial ...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research