Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling

Abstract

Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling-termed substrate-explicit modeling-that enables parameterizing OM-specific oxidative degradation pathways and reaction rates based on the thermodynamic properties of OM pools. The resulting kinetic models are characterized by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics, our method not only predicted oxidative degradation to be primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements, but also revealed previously unknown critically important aspects of biogeochemical reactions, including their condition-specific response to carbon and/or oxygen limitations. Lastly, we showed that the proposed substrate-explicit modeling approach can be synergistically combined with enzyme-explicit approach to provide improved predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling

“…A portion of the research was performed at the Environmental Molecular Science Laboratory User Facility located on PNNL's campus. This manuscript has been released as a pre-print at https://www.biorxiv.org/content/ 10.1101/2020.02.27.968669v1 (Song et al, 2020).…”

Using Community Science to Reveal the Global Chemogeography of River Metabolomes

“…These detailed metabolome characterizations have the potential to enable global-scale inferences about watershed features (e.g., vegetation, lithology, hydrology, microbiology, climate) that govern the reactivity and fate of OM across river corridors [ 35 , 38 ]. In turn, metabolomics can enhance our predictive capabilities of global river corridor biogeochemical cycles by helping to improve the representation of biochemical mechanisms in numerical models, such as reactive transport codes [ 39 , 40 ]. For example, an emerging substrate-explicit model uses thermodynamic theory to explicitly account for the chemical composition of all metabolites in OM pools to improve the predictive capacity of biogeochemical models [ 40 ].…”

WHONDRS-GUI: a web application for global survey of surface water metabolites

“…These data can be used with the FTICR and geochemical data (from the WHONDRS-GUI) to inform biogeochemical reaction network models that can be further integrated with reactive transport codes that simulate reactions and the movement of water and other materials (e.g., nutrients) through river corridors. The develop of such an integrated modeling framework is nascent, and some of the critical theory and modeling tools have recently been developed (Song et al, 2020). There are significant efforts underway to implement this integrated framework via publicly available tools in the US Department of Energy Systems Biology Knowledgebase (KBase; https://kbase.us/).…”