ORT: a workflow linking genome-scale metabolic models with reactive transport codes

Motivation: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As 'omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results: We propose a workflow ('Omics to Reactive Transport-ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN.

Automated summary

The behavior of nutrients and contaminants in the subsurface is influenced by various coupled hydrobiogeochemical processes occurring at different scales. Microbial processes play a crucial role in affecting macroscopic system behavior through mediating precipitation, dissolution, and altering aqueous geochemistry. The proposed workflow ('Omics to Reactive Transport-ORT) combines metagenomic and environmental data with open-source software packages to improve ecosystem models, demonstrating how microbiological processes can be integrated into reactive transport models for accurate predictions.