Geomicrobial Kinetics: Extrapolating Laboratory Studies to Natural Environments

Abstract

Predicting metabolic rates and population sizes of microorganisms in natural environments is a central problem in geomicrobiology. Such predictions can be made on the basis of a thermodynamically consistent rate law that accounts for both kinetic and thermodynamic controls on microbial metabolism. Application of the rate law requires kinetic and growth parameters, the values of which have been determined for pure and mixed cultures growing in laboratory reactors. However, not all parameter values derived from laboratory studies can be validly applied to the environment. This article illustrates a best-choice approach for extrapolating experimentally-derived parameter values to natural environments, using microbial sulfate reduction coupled to acetate oxidation as an example. We compiled kinetic and growth parameters determined by previous laboratory studies and evaluated their applicability to natural environments. Our results suggest that some parameters, such as rate constants and maximum growth yields, can be applied directly to the environment; others, such as half-saturation constants and specific maintenance rates, are best determined using samples recovered from the environment of interest. The best-choice parameter values were applied to simulation of acetotrophic sulfate reduction in the sediments of a freshwater lake. Our analysis shows that the best-choice approach reduces the tasks of parameter fitting and simplifies the modeling exercise. The proposed approach also ensures that parameters in use are consistent with the physiology of indigenous microorganisms, and relevant to the environment of interest.

Modeling biogeochemical processes and isotope fractionation of enhanced in situ biodenitrification in a fractured aquifer

“…In contrast, the specific growth yields (Y h ) and the maximum rate constant (k max ) can be 434 directly transferred from the laboratory to the field scale (Jin et al, 2012). Both parameters are 435 related to the properties of enzymes and pathways of metabolic reactions that are the same in 436 the laboratory and in the field (Table 4).…”

“…During the upscaling process (in calibration process), the parameters were adjusted by 422 considering the difference between the bioavailability of nutrients at the batch and field scales 423 (based on the half-saturation constants) and the adaptation of microbial metabolism to the 424 environment (based on decay constants) (Jin et al, 2012). Table 4 shows that the differences in 425 the half-saturation parameters between the batch and field experiments are always less than 426 one order of magnitude, except for the half-saturation of nitrate during denitrification.…”

“…Considering the high variability of these parameters (Table 5), we believe 430 that the differences observed between the parameters at the batch and field scales are small 431 enough to set up the batch scale parameters as a good initial approximation to start up in the 432 field scale models. 433In contrast, the specific growth yields (Y h ) and the maximum rate constant (k max ) can be 434 directly transferred from the laboratory to the field scale (Jin et al, 2012). Both parameters are 435 related to the properties of enzymes and pathways of metabolic reactions that are the same in 436 the laboratory and in the field (Table 4).…”

Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone

“…Certain pathways found in other OMZs, such as aerobic sulfide oxidation (19), dissimilatory nitrate reduction to ammonium (DNRA), (20) and sulfate reduction (5), were excluded from our model based on information from previous studies (12)(13)(14)(16)(17)(18) as well as preliminary tests with model variants (as explained below). Reaction rates (per gene) depend on the concentrations of utilized metabolites according to first-or second-order (Michaelis-Menten) kinetics (21). In turn, the production or depletion of metabolites at any depth is determined by the reaction rates at that depth.…”

Modeling Biofilms: From Genes to Communities

“…Recently, new kinetic models have been developed to address this problem. Quéméner and Bouchez [98] and Jin et al [99] developed kinetic models with thermodynamics included. The model proposed in [98] is based on the theory of Boltzmann statistics and builds a relationship between microbial growth rate and available energy, thus connecting microbial population dynamics to the thermodynamic driving forces of the surrounding ecosystem.…”