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

Microbial degradation of organic matter (OM) plays a crucial role in biogeochemical cycling and ecosystem

ecosystem

A natural community of plants, animals, and other living organisms and the physical environment in which they live and interact.

Source: A student's guide to Global Climate Change Source

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

respiration

The process whereby living organisms consume CO², convert it to energy, and release it as O².

Source: EPA Environmental Protection Agency Source

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

thermodynamics

The study of the effects of work, heat, and energy on a system. Thermodynamics is only concerned with large scale observations.

Source: NASA Source

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

metagenome

A collection of genomes from many individuals within an environment and/or sample.

Source: NIH Source

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,

mass spectrometry (MS)

Mass spectrometry (MS) is an analytical technique that separates ionized particles such as atoms, molecules, and clusters and can be used to determine the molecular weight of the particles.

Source: National Library of Medicine Source

aerobic respiration rates, metagenomes, and various other metadata attributes. By comprehensively accounting for diverse chemical, thermodynamic, and genomic

genomic

An interdisciplinary field of science that focuses on the structure, function, evolution, mapping, and editing of genomes.

Source: energy.gov Source

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

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

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,

ecosystem modeling

A mathematical representation of an ecological system that can range in scale from an individual population, to an ecological community, or even an entire biome.

Source: USGS Source

accelerating new scientific discoveries by increasing computational efficiency.