ORT

ORT integrates genome-scale metabolic models with reactive transport simulations to predict the influence of microbial processes on nutrient and contaminant dynamics in subsurface hydrobiogeochemical systems.

Key Features:

• KBase–PFLOTRAN coupling: Couples the KBase systems biology platform with the PFLOTRAN reactive transport code to link microbial metabolism to reactive transport simulations.
• Genome-scale metabolic models: Uses genome-scale metabolic models derived from metagenomic data to represent microbial metabolic activity.
• Metagenomic and environmental data integration: Leverages metagenomic and environmental datasets, including microbiological data from NCBI BioProject PRJNA576070, to parameterize models.
• Microbial process representation: Explicitly represents microbial drivers such as nitrification and denitrification and processes including precipitation, dissolution, and changes in aqueous geochemistry.
• Spatiotemporal and iterative coupling: Supports spatiotemporal metagenomic datasets and enables iterative coupling between KBase and PFLOTRAN for time- and space-resolved simulations.
• Implementation artifacts: Provides an ORT Python codebase and KBase narratives as components of the implementation.
• Demonstration dataset: Demonstrated on a river system dataset to predict nitrogen cycling patterns and compare with generalized stoichiometric approaches.

Scientific Applications:

• Subsurface nutrient and contaminant dynamics: Predicts nutrient and contaminant transport and transformation driven by microbial activity in subsurface environments.
• Nitrogen cycling prediction: Models nitrification- and denitrification-driven nitrogen cycling patterns for ecosystem-scale analyses.
• Microbially informed ecosystem modeling: Incorporates microbial precipitation, dissolution, and aqueous geochemistry changes into predictive ecosystem models.
• Spatiotemporal microbial influence assessment: Applies to spatiotemporal metagenomic datasets to assess microbial influences across temporal and spatial scales.

Methodology:

Couples genome-scale metabolic models from KBase with PFLOTRAN reactive transport simulations using metagenomic and environmental datasets (e.g., NCBI BioProject PRJNA576070), enabling iterative KBase–PFLOTRAN exchanges via an ORT Python codebase and KBase narratives.