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Updated: Oct 14, 2025
11:07
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
Published on: December 4, 2021
4.0K
ORT: a workflow linking genome-scale metabolic models with reactive transport codes.
Rebecca L Rubinstein1, Mikayla A Borton2, Haiyan Zhou1
1Subsurface Insights, LLC, Hanover, NH 03755, USA.
Bioinformatics (Oxford, England)
|November 2, 2021
Summary
A new workflow integrates microbial
Area of Science:
- * Subsurface hydrology and biogeochemistry.
- * Environmental microbiology and microbial ecology.
- * Computational modeling and systems biology.
Background:
- * Subsurface nutrient and contaminant transport is governed by complex hydrobiogeochemical processes across various scales.
- * Microbial activities significantly influence these processes, affecting mineral precipitation/dissolution and aqueous geochemistry.
- * The increasing availability of 'omics data necessitates efficient methods for integrating microbial insights into ecosystem models.
Purpose of the Study:
- * To introduce a novel workflow, 'Omics to Reactive Transport (ORT), for integrating metagenomic data into macroscopic reactive transport models.
- * To demonstrate the coupling of KBase, a systems biology platform, with PFLOTRAN, a reactive transport model.
- * To enhance the predictive accuracy of subsurface ecosystem models by incorporating microbial process data.
Main Methods:
- * Development of the ORT workflow connecting KBase and PFLOTRAN.
- * Utilizing metagenomic and geochemical data from a river system for model input.
- * Implementing microbial drivers for nitrification and denitrification within the reactive transport model.
Main Results:
- * Successful demonstration of the ORT workflow using real-world environmental data.
- * Accurate prediction of nitrogen cycling patterns based on microbial processes.
- * The workflow shows potential for application to spatiotemporal metagenomic datasets for iterative model refinement.
Conclusions:
- * The ORT workflow provides an effective method for incorporating microbial 'omics data into reactive transport models.
- * This approach improves the understanding and prediction of subsurface biogeochemical processes.
- * ORT facilitates the integration of microbial ecology and reactive transport modeling for enhanced environmental predictions.
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