Multiomics Data Are Key to Advancing Reactive Transport Models

The Science

Reactive transport models (RTMs) are used to describe and predict the distribution of chemicals in time and space, in both marine and terrestrial (surface and near-surface) environments where microbially mediated processes govern biogeochemical patterns. Yet, challenges exist in modeling microbially driven systems, as well as in integrating data across the vast range of scales relevant to models of biogeochemical cycling.

In the April 2019 topical issue of the journal Elements on reactive transport modeling, Tim Scheibe of Pacific Northwest National Laboratory (PNNL) and coauthor Chistof Meile of the University of Georgia discuss common approaches that have been used to incorporate microbial community interactions and their influence on geochemical processes in RTMs, as well as future opportunities to leverage new instrument and data capabilities—including multiomics—to create new and more realistic modeling approaches.

In particular, the authors argue that RTMs with multiomics will help advance understanding of how complex microbial communities respond to environmental changes. These new models will also help identify microbial impacts on local and global elemental cycling, the fate of contaminants, redox transformations, and other processes mediated by microorganisms.

The Impact

Integrating multiomics data into RTMs will improve predictive understanding of critical watershed processes such as carbon and nitrogen cycling within those watersheds and more broadly. Modeling informed by multiomics will also reveal how critical microbial processes change in response to environmental perturbations—an urgent imperative for watersheds subject to increasingly frequent or sustained perturbations.

Summary

Representation of microbial processes in RTMs has advanced significantly over the past few decades, accounting for dynamic changes in biomass, functional regulation in response to environmental changes, and thermodynamic constraints. Current RTMs represent microbial functions with greater process fidelity and reduced empiricism.

The authors say that incorporating multiomics data is a current frontier in RTMs, and offers great potential for improving scientific understanding of microbial processes and predictive modeling. To that end, they are engaged in research to integrate complex metagenomics, metabolomics, and other omics data into reaction network models. In turn, these can be linked with state-of-the-art RTMs to simulate system-scale behavior.

In the article, the authors introduce relevant case studies and discuss ways to integrate multiomics data to inform and validate RTMs. Their results advance and enhance those modeling capabilities by identifying and promoting how to integrate multiomics data into microbial models.

The result, the authors say, will be an improved predictive understanding of critical watershed processes such as carbon and nitrogen cycling within specific watersheds and more broadly. Modeling informed by multiomics will also reveal how critical microbial processes change in response to environmental perturbations.

Funded by the Department of Energy’s (DOE) Biological and Environmental Research (BER) program, this article addresses BER’s mission to advance predictive understanding of how hydro-biogeochemically complex watersheds function by promoting a vision of microbial process modeling informed by omics data. The article also promotes the use of DOE-funded capabilities such as DOE’s Systems Biology Knowledgebase (KBase), and DOE user facilities such as the Environmental Molecular Sciences Laboratory (EMSL) and Joint Genome Institute.

With support from DOE’s Subsurface Biogeochemical Research (SBR) program, Scheibe and fellow scientists recently organized a  workshop to build a community of researchers around these ideas and to promote new advancements.

Principal Investigator(s)

Timothy D. Scheibe
Pacific Northwest National Laboratory
[email protected]

Funding

U.S. Department of Energy, Office of Science, Biological and Environmental Research (BER), Subsurface Biogeochemical Research (SBR) program.

References

Meile, C., and T. D. Scheibe. “Reactive transport modeling of microbial dynamics.” Elements 15(2), 111–16 (2019). [DOI: 10.2138/gselements.15.2.111]