Skip to Content

Computational Identification of Bioavailable Organic Matter and Their Traits for Predictive Biogeochemical and Ecosystem Modeling

Active Dates 9/1/2023-8/31/2025
Program Area Environmental Systems Science
Project Description
Microbial degradation of organic matter (OM) plays a crucial role in biogeochemical cycling and ecosystem function. A fundamental understanding of the interplay between microbial and enzymatic processes and substrate chemistry is essential for accurate prediction of OM degradation. Despite increasing availability of high-quality omics data, oversimplified descriptions of substrate pools continue to pose a serious bottleneck in building predictive biogeochemical models. In this regard, the substrate-explicit thermodynamic modeling has emerged as a promising approach that uses high-resolution metabolite data to predict OM degradation and respiration rates. Like other thermodynamics-based biogeochemical models, the substrate-explicit modeling assumes: (1) all chemical compounds detected in the samples are respirable, and (2) their degradation rates are determined by thermodynamic favorability. However, a recent study showed that this model fails to correctly predict respiration rates of sediment samples collected from widely distributed river systems, suggesting that the mentioned assumptions may not be universally valid and need further investigation. Agreeably, not all OM are bioavailable, and their degradation may be governed by multiple factors, rather than thermodynamics only. We do not know how to determine bioavailable OM (bOM), what additional factors may control OM bioavailability, how OM chemistry can be connected to microbial metabolic reactions, and how they together impact OM degradation in space and time. To address these issues, we propose to develop new modeling methods/capabilities to effectively incorporate detailed OM chemistry into biogeochemical and reactive transport models. To achieve this goal, we set up three objectives: identification of bOM and their governing traits (Objective 1), incorporation of bOM into metagenome metabolic networks (Objective 2), and coupling the resulting expanded metabolic networks with reactive-transport models (Objective 3). The effectiveness of our methods will be evaluated using public biogeochemical and omics data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium, including ultrahigh-resolution OM data from Fourier-transform ion cyclotron resonance mass spectrometry, aerobic respiration rates, metagenomes, and various other metadata attributes. By comprehensively accounting for diverse chemical, thermodynamic, and genomic drivers of OM degradation, the project will enable systematically analyzing a variety of river systems with differing biological and chemical characteristics. As a key outcome, the project will produce novel computational capabilities, such as the use of metagenome metabolic networks to incorporate detailed OM chemistry into biogeochemical and reactive transport modeling. We also demonstrate how machine learning techniques can facilitate this integration by achieving computational efficiency. All new developments of simulation and computational tools will be shared publicly with related academic and research communities. Overall, this project will significantly enhance our understanding of the fundamental mechanisms that govern OM degradation by shifting our focus from individual parameters to the interactions of multiple factors; enhance our ability to use molecular-level data and models to inform large-scale ecosystem modeling, accelerating new scientific discoveries by increasing computational efficiency.
Award Recipient(s)
  • University of Nebraska Lincoln (PI: Song, Hyun-Seob)