Explicit Representation of Microbial Decomposition of Organic Matter in Biogeochemical Modeling

The Science

Microbial degradation of organic matter drives biogeochemical cycling on  regional and global scales. However, no modeling framework can capture the molecular details of complex organic matter chemistry in a way that predicts biogeochemical cycling at the ecosystem level; this is due mainly to the difficulty in suitably parameterizing those molecular details. Now a team of researchers have developed a new concept of thermodynamics-based biogeochemical modeling that formulates reaction rates in terms of only two parameters, regardless of the complexity of organic matter profiles. This approach, termed substrate-explicit modeling, incorporates high- resolution mass spectrometry (MS) data for large-scale biogeochemical and reactive transport simulations. In a comparative analysis of two biogeochemically distinct sites, the team used substrate-explicit modeling to incorporate thousands of compounds and showed that thermodynamic properties are a main driver of aerobic respiration.

The Impact

Representing reaction rates based on a limited number of thermodynamic parameters removes a barrier in accounting for complex organic matter chemistry in large-scale ecosystem simulations. This feature can significantly facilitate unprecedented predictions of biogeochemical and ecosystem dynamics, supporting enhanced integration of data and modeling for research programs including the National Ecological Observation Network (NEON) and the Worldwide Hydro- biogeochemistry Observation Network for Dynamic River Systems (WHONDRS).

Summary

Existing approaches to model organic matter decomposition dynamics can account for details of microbial and/or enzymatic processes to a degree; however, none can yet describe complex organic matter chemistry revealed by high-throughput omics data. This significantly limits understanding of the dynamic interplay between microbes, enzymes, and substrates in biogeochemical cycling.

To fill this gap, scientists from the U.S. Department of Energy’s Pacific Northwest National Laboratory and collaborators from other institutions proposed substrate-explicit modeling. This approach incorporates high-resolution metabolomics data to represent complex chemistry based on the thermodynamic properties of organic matter pools. It also combines a suite of previously developed thermodynamic theories to characterize reaction kinetics with only two parameters—maximal growth rate and harvest volume—regardless of the number of chemical compounds in an organic matter pool.

The team compared predictions from this model with experimental results from two sites with distinct organic matter thermodynamics. The predictions were consistent with their previously reported findings for how thermodynamic properties of an organic matter pool control aerobic respiration across carbon- and/or oxygen-limited conditions. The researchers also combined substrate-, microbe-, and enzyme-explicit models to further improve the predictions. The new modeling concept proposed in this work could provide unprecedented data-model integration and be a foundational platform for predictive biogeochemical and ecosystem simulations across scales.

Principal Investigator(s)

Tim Scheibe
Pacific Northwest National Laboratory
[email protected]

Funding

This research was supported as part of the Subsurface Biogeochemical Research (SBR) program of the Office of Biological and Environmental Research within the U.S. Department of Energy (DOE) Office of Science. This contribution originates from the SBR Scientific Focus Area at the Pacific Northwest National Laboratory (PNNL) and was supported by the partnership with the Interoperable Design of Extreme-scale Application Software (IDEAS)-Watersheds project. A portion of the research was performed at the Environmental Molecular Sciences Laboratory, a DOE Office of Science user facility located on PNNL’s campus.

References

Song, H.-S. et al. “Representing organic matter thermodynamics in biogeochemical reactions via substrate-explicit modeling.” Frontiers in Microbiology 11, 531756 (2020). [DOI:10.3389/fmicb.2020.531756]