Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

Li, minjing; Qian, Weijun; Gao, Yuqian; Shi, Liang; Liu, Chongxuan

doi:10.1021/acs.est.7b03158

Cited by 29 publications

(21 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, sequencing data have demonstrated high spatio‐temporal dynamics of soil and marine microbial diversity (Shade et al, ) and different rates of N 2 O production and consumption by microbial communities (Levine et al, ). Recently, successful efforts to integrate environmental genomics, enzyme kinetics and biogeochemical models have been developed (Li et al, ; Reed et al, ; Zhu et al, ).…”

Section: Current and Future Methods For Assessing Sources And Sinks Omentioning

confidence: 99%

Systems biology approaches towards predictive microbial ecology

Otwell

Lomana

Gibbons

et al. 2018

Environmental Microbiology

View full text Add to dashboard Cite

Through complex interspecies interactions, microbial processes drive nutrient cycling and biogeochemistry. However, we still struggle to predict specifically which organisms, communities and biotic and abiotic processes are determining ecosystem function and how environmental changes will alter their roles and stability. While the tools to create such a predictive microbial ecology capability exist, cross-disciplinary integration of high-resolution field measurements, detailed laboratory studies and computation is essential. In this perspective, we emphasize the importance of pursuing a multiscale, systems approach to iteratively link ecological processes measured in the field to testable hypotheses that drive high-throughput laboratory experimentation. Mechanistic understanding of microbial processes gained in controlled lab systems will lead to the development of theory that can be tested back in the field. Using N O production as an example, we review the current status of field and laboratory research and layout a plausible path to the kind of integration that is needed to enable prediction of how N-cycling microbial communities will respond to environmental changes. We advocate for the development of realistic and predictive gene regulatory network models for environmental responses that extend from single-cell resolution to ecosystems, which is essential to understand how microbial communities involved in N O production and consumption will respond to future environmental conditions.

show abstract

Section: Current and Future Methods For Assessing Sources And Sinks Omentioning

confidence: 99%

Systems biology approaches towards predictive microbial ecology

Otwell

Lomana

Gibbons

et al. 2018

Environmental Microbiology

View full text Add to dashboard Cite

show abstract

“…The biogeochemical reaction network and the reaction rates in the model were adopted from the work of Li et al () and H.‐S. Song et al ().…”

Section: Implementation Of Groundwater Biogeochemical Reactive Transportmentioning

confidence: 99%

“…The biogeochemical reaction network and the reaction rates in the model were adopted from the work of Li et al (2017) and H.-S. Song et al (2017). The reactions of oxidative respiration and denitrification for the dissolved organic carbon (CH 2 O) and the synthesis of biomass (C 5 H 7 O 2 N) were considered as follows:…”

Section: Water Resources Researchmentioning

confidence: 99%

Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models

Dai

Song

et al. 2019

Water Resources Research

View full text Add to dashboard Cite

Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance‐based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real‐world biogeochemical model at the groundwater‐surface water interface within the Hanford Site's 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon‐consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multiphysics models for complex systems.

show abstract

“…Genomic data provide information on metabolic potential, but not on actual microbial activity. To account for activity, the gene-centric approach has been further developed to predict concentrations of functional transcripts and enzymes (Louca et al, 2016;Li et al, 2017c;Song et al, 2017). In one of the studies, the model was validated using metatranscriptomic and metaproteomic data (Louca et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

et al. 2021

View full text Add to dashboard Cite

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.

show abstract

Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

Cited by 29 publications

References 70 publications

Systems biology approaches towards predictive microbial ecology

Systems biology approaches towards predictive microbial ecology

Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Contact Info

Product

Resources

About