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

Abstract

Motivation: Advanced modeling tools are available for `omics-based metabolic modeling and for reactive transport modeling, but there is a disconnect between these methods, which hinders linking models across scales. Microbial processes strongly impact many natural systems, and so better capture of microbial dynamics could greatly improve simulations of these systems. Results: Our approach, ORT, applied to environmental metagenomic data from a river system predicted nitrogen cycling patterns with site-specific insight into chemical and biological drivers of nitrification and denitrification processes. Availability and Implementation: Live interactive models are available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data is available at NCBI via BioProject ID PRJNA576070. The code for ORT (written in Python 3) is avail-able at https://github.com/subsurfaceinsights. The KBase narrative used for the test case is publicly available at https://narrative.kbase.us/narrative/71260.