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

Rubinstein, Rebecca L.; Borton, Mikayla A.; Zhou, Haiyan; Shaffer, Michael; Hoyt, David W.; Stegen, James; Henry, Christopher S.; Wrighton, Kelly C.; Versteeg, Roelof; Alkan, ed., Can

doi:10.1093/bioinformatics/btab753

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

Journal Article · Tue Nov 02 00:00:00 EDT 2021 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btab753· OSTI ID:1839849

; Borton, Mikayla A.; Zhou, Haiyan; Shaffer, Michael; Hoyt, David W.; Stegen, James; Henry, Christopher S.; Wrighton, Kelly C.; Versteeg, Roelof; Alkan, ed., Can

Abstract 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.

Availability and implementation

Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258.

Supplementary information

Supplementary data are available at Bioinformatics online.

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Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; AC05-76RL01830; SC0018020; SC0019619

OSTI ID:: 1839849

Alternate ID(s):: OSTI ID: 1828774
OSTI ID: 1877104
OSTI ID: 1909023

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 3 Vol. 38; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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