Theoretical insights from upscaling Michaelis–Menten microbial dynamics in biogeochemical models: a dimensionless approach

Leading an effective response to the accelerating crisis of anthropogenic climate change will require improved understanding of global carbon cycling. A critical source of uncertainty in Earth system models (ESMs) is the role of microbes in mediating both the formation and decomposition of soil organic matter, and hence in determining patterns of CO2 efflux. Traditionally, ESMs model carbon turnover as a first-order process impacted primarily by abiotic factors, whereas contemporary biogeochemical models often explicitly represent the microbial biomass and enzyme pools as the active agents of decomposition. However, the combination of non-linear microbial kinetics and ecological heterogeneity across space and time guarantees that upscaled dynamics will violate mean-field assumptions via Jensen's inequality. Violations of mean-field assumptions mean that parameter estimates from models fit to upscaled data (e.g., eddy covariance towers) are likely systematically biased. Likewise, predictions of CO2 efflux from models conditioned on mean-field values will also be biased. Here we present a generic mathematical analysis of upscaling Michaelis–Menten kinetics under heterogeneity and provide solutions in dimensionless form. We illustrate how our dimensionless form facilitates qualitative insight into the significance of this scale transition and argue that it will facilitate cross-site intercomparisons of flux data. We also identify the critical terms that need to be constrained in order to unbias parameter estimates.

A General Model for Biofilm-Driven Microbial Electrosynthesis of Carboxylates From CO2

Up to now, computational modeling of microbial electrosynthesis (MES) has been underexplored, but is necessary to achieve breakthrough understanding of the process-limiting steps. Here, a general framework for modeling microbial kinetics in a MES reactor is presented. A thermodynamic approach is used to link microbial metabolism to the electrochemical reduction of an intracellular mediator, allowing to predict cellular growth and current consumption. The model accounts for CO2 reduction to acetate, and further elongation to n-butyrate and n-caproate. Simulation results were compared with experimental data obtained from different sources and proved the model is able to successfully describe microbial kinetics (growth, chain elongation, and product inhibition) and reactor performance (current density, organics titer). The capacity of the model to simulate different system configurations is also shown. Model results suggest CO2 dissolved concentration might be limiting existing MES systems, and highlight the importance of the delivery method utilized to supply it. Simulation results also indicate that for biofilm-driven reactors, continuous mode significantly enhances microbial growth and might allow denser biofilms to be formed and higher current densities to be achieved.

Theoretical insights from upscaling Michaelis-Menten microbial dynamics in biogeochemical models: a dimensionless approach

Leading an effective response to the accelerating crisis of anthropogenic climate change will require improved understanding of global carbon cycling. A critical source of uncertainty in Earth Systems Models (ESMs) is the role of microbes in mediating both the formation and decomposition of soil organic matter, and hence in determining patterns of CO2 efflux. Traditionally, ESMs model carbon turnover as a first order process impacted primarily by abiotic factors, whereas contemporary biogeochemical models often explicitly represent the microbial biomass and enzyme pools as the active agents of decomposition. However, the combination of non-linear microbial kinetics and ecological heterogeneity across space guarantees that upscaled dyamics will violate mean-field assumptions via Jensen’s Inequality. Violations of mean-field assumptions mean that parameter estimates from models fit to upscaled data (e.g. eddy covariance towers) are likely systematically biased. Here we present a generic mathematical analysis of upscaling michaelis-menten kinetics under heterogeneity, and provide solutions in dimensionless form. We illustrate how our dimensionless form facilitates qualitative insight into the significance of this scale transition, and argue that it will facilitate cross site intercomparisons of flux data. We also identify the critical terms that need to be constrained in order to unbias parameter estimates.

Temperature and Inoculum Origin Influence the Performance of Ex-Situ Biological Hydrogen Methanation

The conversion of H2 into methane can be carried out by microorganisms in a process so-called biomethanation. In ex-situ biomethanation H2 and CO2 gas are exogenous to the system. One of the main limitations of the biomethanation process is the low gas-liquid transfer rate and solubility of H2 which are strongly influenced by the temperature. Hydrogenotrophic methanogens that are responsible for the biomethanation reaction are also very sensitive to temperature variations. The aim of this work was to evaluate the impact of temperature on batch biomethanation process in mixed culture. The performances of mesophilic and thermophilic inocula were assessed at 4 temperatures (24, 35, 55 and 65 °C). A negative impact of the low temperature (24 °C) was observed on microbial kinetics. Although methane production rate was higher at 55 and 65 °C (respectively 290 ± 55 and 309 ± 109 mL CH4/L.day for the mesophilic inoculum) than at 24 and 35 °C (respectively 156 ± 41 and 253 ± 51 mL CH4/L.day), the instability of the system substantially increased, likely because of a strong dominance of only Methanothermobacter species. Considering the maximal methane production rates and their stability all along the experiments, an optimal temperature range of 35 °C or 55 °C is recommended to operate ex-situ biomethanation process.

Microbial Kinetic Analysis of a Hybrid UASB Reactor

<p>Microbial kinetics of a hybrid upflow anaerobic sludge blanket (UASB) reactor were investigated when treating chemical synthesis–based pharmaceutical wastewater. Monod, Grau first-order, Grau second-order, Stover-Kincannon, Chen &amp; Hashimoto kinetic models were applied to the hybrid reactor. The second-order model was found to be the most appropriate model for the hybrid reactor (R2 = 0.99) and offers the best description of the process. The substrate removal rate constant (k2S) was found to be 4.91 d-1.</p>