Dataset Information

C-STABILITY an innovative modeling framework to leverage the continuous representation of organic matter.

ABSTRACT: The understanding of soil organic matter (SOM) dynamics has considerably advanced in recent years. It was previously assumed that most SOM consisted of recalcitrant compounds, whereas the emerging view considers SOM as a range of polymers continuously processed into smaller molecules by decomposer enzymes. Mainstreaming this new paradigm in current models is challenging because of their ill-adapted framework. We propose the C-STABILITY model to resolve this issue. Its innovative framework combines compartmental and continuous modeling approaches to accurately reproduce SOM cycling processes. C-STABILITY emphasizes the influence of substrate accessibility on SOM turnover and makes enzymatic and microbial biotransformations of substrate explicit. Theoretical simulations provide new insights on how depolymerization and decomposers ecology impact organic matter chemistry and amount during decomposition and at steady state. The flexible mathematical structure of C-STABILITY offers a promising foundation for exploring new mechanistic hypotheses and supporting the design of future experiments.

SUBMITTER: Sainte-Marie J

PROVIDER: S-EPMC7864906 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

C-STABILITY an innovative modeling framework to leverage the continuous representation of organic matter.

Sainte-Marie Julien J Barrandon Matthieu M Saint-André Laurent L Gelhaye Eric E Martin Francis F Derrien Delphine D

Nature communications 20210205 1

The understanding of soil organic matter (SOM) dynamics has considerably advanced in recent years. It was previously assumed that most SOM consisted of recalcitrant compounds, whereas the emerging view considers SOM as a range of polymers continuously processed into smaller molecules by decomposer enzymes. Mainstreaming this new paradigm in current models is challenging because of their ill-adapted framework. We propose the C-STABILITY model to resolve this issue. Its innovative framework combin ...[more]

PMID: 33547289

Similar Datasets

Project description: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. Based on previous developments in the literature, we characterized the resulting kinetic models 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. The two parameters include maximal growth rate (μmax) and harvest volume (Vh) (i.e., the volume that a microbe can access for harvesting energy). 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 using ultra high-resolution metabolomics datasets derived from Fourier transform ion cyclotron resonance mass spectrometry analyses, our method predicted how oxidative degradation is primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements (as shown by correlation coefficients of up to 0.61), and how biogeochemical reactions can vary in response to carbon and/or oxygen limitations. Lastly, we showed that incorporation of enzymatic regulations into substrate-explicit models is critical for more reasonable 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.

Dataset Information

C-STABILITY an innovative modeling framework to leverage the continuous representation of organic matter.

Publications

C-STABILITY an innovative modeling framework to leverage the continuous representation of organic matter.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets