Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling.

The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.

Linking population dynamics to microbial kinetics for hybrid modeling of bioelectrochemical systems.

Mechanistic and data-driven models have been developed to provide predictive insights into the design and optimization of engineered bioprocesses. These two modeling strategies can be combined to form hybrid models to address the issues of parameter identifiability and prediction interpretability. Herein, we developed a novel and robust hybrid modeling strategy by incorporating microbial population dynamics into model construction. The hybrid model was constructed using bioelectrochemical systems (BES) as a platform system. We collected 77 samples from 13 publications, in which the BES were operated under diverse conditions, and performed holistic processing of the 16S rRNA amplicon sequencing data. Community analysis revealed core populations composed of putative electroactive taxa Geobacter, Desulfovibrio, Pseudomonas, and Acinetobacter. Primary Bayesian networks were trained with the core populations and environmental parameters, and directed Bayesian networks were trained by defining the operating parameters to improve the prediction interpretability. Both networks were validated with Bray-Curtis similarly, relative root-mean-square error (RMSE), and a null model. A hybrid model was developed by first building a three-population mechanistic component and subsequently feeding the estimated microbial kinetic parameters into network training. The hybrid model generated a simulated community that shared a Bray-Curtis similarity of 72% with the actual microbial community at the genus level and an average relative RMSE of 7% for individual taxa. When examined with additional samples that were not included in network training, the hybrid model achieved accurate prediction of current production with a relative error-based RMSE of 0.8 and outperformed the data-driven models. The genomics-enabled hybrid modeling strategy represents a significant step toward robust simulation of a variety of engineered bioprocesses.

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.

Sludge characteristics, system performance and microbial kinetics of ultra-short-SRT activated sludge processes.

Activated sludge processes with an ultra-short sludge retention time (ultra-short-SRT) are considered to have potential for energy and resource recovery from wastewater. The present study focused on the sludge characteristics, system performance and microbial kinetics in ultra-short-SRT activated sludge (USSAS) processes using typical domestic wastewater (SRT = 0.5, 1, 2, 3 and 4 d). The results showed that compared with the sludge in conventional activated sludge (CAS) processes, the sludge structure in USSAS system was looser (fractal dimension, D2P, 1.19-1.33), the boundary was rougher (pore boundary fractal dimension, DB, 1.44-1.59), the sludge concentration was lower, and the sludge volume index (SVI) was higher; bacteria such as Thiothrix and Trichococcus that cause sludge bulking, which poses an operation risk, were extensively detected, especially at SRTs of 0.5 d and 1.0 d. The performance in terms of total chemical oxygen demand (tCOD) and phosphorus removal increased with increasing SRT, and the highest removal rate (approximately 85% for tCOD and 90% for phosphorus) was observed when the SRT was 4 d. Both bioconversion and biosorption were responsible for the C/P separation, and their roles were different for different types of organic matter and phosphorus under different SRT conditions. The proportion of phosphate-accumulating organisms (PAO) reached 2.4% when the SRT was 3 d, resulting in highly effective biological phosphorus removal. The values of microbial kinetic parameters such as YH and KdH in USSAS systems were higher than those in CAS systems, indicating faster microbial community renewal. This study was helpful for understanding the characteristics of USSAS process.

Multiscale modeling of microbial degradation of outer tissues of fiber-crop stems during the dew retting process.

Dew retting of fiber crops, such as hemp or flax, in the field after harvest promotes the microbial biodegradation of the tissues surrounding cellulosic fibers, which helps preserve the quality of fibers during their extraction and valorization for industry. This bioprocess is currently the bottleneck for plant fiber valorization because it is empirically managed and its controlling factors have not been properly quantified. A novel multiscale model representing tissue and polymer biodegradation was developed to simulate microbial growth on the stem during retting. The model was evaluated against experimental hemp retting data. It consistently simulated the mass loss of eight plant polymers belonging to two tissues of the stem outer layer, i.e., parenchyma and fiber bundles. Microbial growth was modeled by Monod equations and modulated by the functions of temperature and moisture. This work provides a tool for gaining more insights into microorganism behavior during retting under local climate conditions.

Guidelines for the design of (optimal) isothermal inactivation experiments.

Kinetic models are nowadays a basic tool to ensure food safety. Most models used in predictive microbiology have model parameters, whose precision is crucial to provide meaningful predictions. Kinetic parameters are usually estimated based on experimental data, where the experimental design can have a great impact on the precision of the estimates. In this sense, Optimal Experiment Design (OED) applies tools from optimization and information theory to identify the most informative experiment under a set of constrains (e.g. mathematical model, number of samples, etc). In this work, we develop a methodology for the design of optimal isothermal inactivation experiments. We consider the two dimensions of the design space (time and temperature), as well as a temperature-dependent maximum duration of the experiment. Functions for its application have been included in the bioOED R package. We identify design patterns that remain optimum regardless of the number of sampling points for three inactivation models (Bigelow, Mafart and Peleg) and three model microorganisms (Escherichia coli, Salmonella Senftenberg and Bacillus coagulans). Samples at extreme temperatures and close to the maximum duration of the experiment are the most informative. Moreover, the Mafart and Peleg models require some samples at intermediate time points due to the non-linearity of the survivor curve. The impact of the reference temperature on the precision of the parameter estimates is also analysed. Based on numerical simulations we recommend fixing it to the mean of the maximum and minimum temperatures used for the experiments. The article ends with a discussion presenting guidelines for the design of isothermal inactivation experiments. They combine these optimum results based on information theory with several practical limitations related to isothermal inactivation experiments. The application of these guidelines would reduce the experimental burden required to characterize thermal inactivation.

Mathematical modeling of ethanol production in solid-state fermentation based on solid medium' dry weight variation.

In this work, mathematical modeling of ethanol production in solid-state fermentation (SSF) has been done based on the variation in the dry weight of solid medium. This method was previously used for mathematical modeling of enzyme production; however, the model should be modified to predict the production of a volatile compound like ethanol. The experimental results of bioethanol production from the mixture of carob pods and wheat bran by Zymomonas mobilis in SSF were used for the model validation. Exponential and logistic kinetic models were used for modeling the growth of microorganism. In both cases, the model predictions matched well with the experimental results during the exponential growth phase, indicating the good ability of solid medium weight variation method for modeling a volatile product formation in solid-state fermentation. In addition, using logistic model, better predictions were obtained.

Linkage of microbial kinetics and bacterial community structure of MBR and hybrid MBBR-MBR systems to treat salinity-amended urban wastewater.

Three pilot-scale bioreactors were started up and operated under salinity-amended urban wastewater feeding. The bioreactors were configured as membrane bioreactor and two different hybrid, moving bed biofilm reactor-membrane bioreactor and operated with a hydraulic retention time of 9.5 h, a solid residence time of 11.75 days and a total solids concentration of 2500 mg L-1 . The three systems showed excellent performance in suspended solids, BOD5 , and COD removal (values of 96-100%, 97-99%, and 88-90%, respectively), but poor nitrogen removal (values of 20-30%). The bacterial community structure during the start-up phase and the stabilization phase were different, as showed by ß-diversity analyses. The differences between aerobic and anoxic biomass-and between suspended and attached biomass-were higher at the start-up phase than at the stabilization phase. The start-up phase showed high abundances of Chiayiivirga (mean values around 3-12% relative abundance) and Luteimonas (5-8%), but in the stabilization phase, the domination belonged to Thermomonas (3-14%), Nitrobacter (3-7%), Ottowia (3-11.5%), and Comamonas (2-6%), among others. Multivariate redundancy analyses showed that Thermomonas and Nitrosomonas were positively correlated with fast autotrophic kinetics, while Caulobacter and Ottowia were positively correlated with fast heterotrophic kinetics. Nitrobacter, Rhodanobacter, and Comamonas were positively correlated with fast autotrophic and heterotrophic kinetics. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1483-1495, 2017.

Hydrogen production and microbial kinetics of Clostridium termitidis in mono-culture and co-culture with Clostridium beijerinckii on cellulose.

Cellulose utilization by hydrogen producers remains an issue due to the low hydrogen yields reported and the pretreatment of cellulose prior to fermentation requires complex and expensive steps. Clostridium termitidis is able to breakdown cellulose into glucose and produce hydrogen. On the other hand, Clostridium beijerinckii is not able to degrade cellulose but is adept at hydrogen production from glucose; therefore, it was chosen to potentially enhance hydrogen production when co-cultured with C. termitidis on cellulose. In this study, batch fermentation tests were conducted to investigate the direct hydrogen production enhancement of mesophilic cellulolytic bacteria C. termitidis co-cultured with mesophilic hydrogen producer C. beijerinckii on cellulose at 2 g l-1 compared to C. termitidis mono-culture. Microbial kinetics parameters were determined by modeling in MATLAB. The achieved highest hydrogen yield was 1.92 mol hydrogen mol-1 hexose equivalentadded in the co-culture compared to 1.45 mol hydrogen mol-1 hexose equivalentadded in the mono-culture. The maximum hydrogen production rate of 26 ml d-1 was achieved in the co-culture. Co-culture exhibited an overall 32 % enhancement of hydrogen yield based on hexose equivalent added and 15 % more substrate utilization. The main metabolites were acetate, ethanol, lactate, and formate in the mono-culture, with also butyrate in the co-culture. Additionally, the hydrogen yield of C. beijerinckii only in glucose was 2.54 mol hydrogen mol-1 hexose equivalent. This study has proved the viability of co-culture of C. termitidis with C. beijerinckii for hydrogen production directly from a complex substrate like cellulose under mesophilic conditions.

Thermodynamic and Kinetic Response of Microbial Reactions to High CO2.

Geological carbon sequestration captures CO2 from industrial sources and stores the CO2 in subsurface reservoirs, a viable strategy for mitigating global climate change. In assessing the environmental impact of the strategy, a key question is how microbial reactions respond to the elevated CO2 concentration. This study uses biogeochemical modeling to explore the influence of CO2 on the thermodynamics and kinetics of common microbial reactions in subsurface environments, including syntrophic oxidation, iron reduction, sulfate reduction, and methanogenesis. The results show that increasing CO2 levels decreases groundwater pH and modulates chemical speciation of weak acids in groundwater, which in turn affect microbial reactions in different ways and to different extents. Specifically, a thermodynamic analysis shows that increasing CO2 partial pressure lowers the energy available from syntrophic oxidation and acetoclastic methanogenesis, but raises the available energy of microbial iron reduction, hydrogenotrophic sulfate reduction and methanogenesis. Kinetic modeling suggests that high CO2 has the potential of inhibiting microbial sulfate reduction while promoting iron reduction. These results are consistent with the observations of previous laboratory and field studies, and highlight the complexity in microbiological responses to elevated CO2 abundance, and the potential power of biogeochemical modeling in evaluating and quantifying these responses.

The yield and decay coefficients of exoelectrogenic bacteria in bioelectrochemical systems.

In conventional wastewater treatment, waste sludge management and disposal contribute the major cost for wastewater treatment. Bioelectrochemical systems, as a potential alternative for future wastewater treatment and resources recovery, are expected to produce small amounts of waste sludge because exoelectrogenic bacteria grow on anaerobic respiration and form highly populated biofilms on bioanode surfaces. While waste sludge production is governed by the yield and decay coefficient, none of previous studies have quantified these kinetic constants for exoelectrogenic bacteria. For yield coefficient estimation, we modified McCarty's free energy-based model by using the bioanode potential for the free energy of the electron acceptor reaction. The estimated true yield coefficient ranged 0.1 to 0.3 g-VSS (volatile suspended solids) g-COD(-1) (chemical oxygen demand), which is similar to that of most anaerobic microorganisms. The yield coefficient was sensitively affected by the bioanode potential and pH while the substrate and bicarbonate concentrations had relatively minor effects on the yield coefficient. In lab-scale experiments using microbial electrolysis cells, the observed yield coefficient (including the effect of cell decay) was found to be 0.020 ± 0.008 g-VSS g-COD(-1), which is an order of magnitude smaller than the theoretical estimation. Based on the difference between the theoretical and experimental results, the decay coefficient was approximated to be 0.013 ± 0.002 d(-1). These findings indicate that bioelectrochemical systems have potential for future wastewater treatment with reduced waste sludge as well as for resources recovery. Also, the found kinetic information will allow accurate estimation of wastewater treatment performance in bioelectrochemical systems.

Advances and Practices of Bioprocess Scale-up.

: This chapter addresses the update progress in bioprocess engineering. In addition to an overview of the theory of multi-scale analysis for fermentation process, examples of scale-up practice combining microbial physiological parameters with bioreactor fluid dynamics are also described. Furthermore, the methodology for process optimization and bioreactor scale-up by integrating fluid dynamics with biokinetics is highlighted. In addition to a short review of the heterogeneous environment in large-scale bioreactor and its effect, a scale-down strategy for investigating this issue is addressed. Mathematical models and simulation methodology for integrating flow field in the reactor and microbial kinetics response are described. Finally, a comprehensive discussion on the advantages and challenges of the model-driven scale-up method is given at the end of this chapter.

Empirical model based on Weibull distribution describing the destruction kinetics of natural microbiota in pineapple (Ananas comosus L.) puree during high-pressure processing.

High pressure inactivation of natural microbiota viz. aerobic mesophiles (AM), psychrotrophs (PC), yeasts and molds (YM), total coliforms (TC) and lactic acid bacteria (LAB) in pineapple puree was studied within the experimental domain of 0.1-600 MPa and 30-50 °C with a treatment time up to 20 min. A complete destruction of yeasts and molds was obtained at 500 MPa/50 °C/15 min; whereas no counts were detected for TC and LAB at 300 MPa/30 °C/15 min. A maximum of two log cycle reductions was obtained for YM during pulse pressurization at the severe process intensity of 600 MPa/50 °C/20 min. The Weibull model clearly described the non-linearity of the survival curves during the isobaric period. The tailing effect, as confirmed by the shape parameter (ß) of the survival curve, was obtained in case of YM (ß<1); whereas a shouldering effect (ß>1) was observed for the other microbial groups. Analogous to thermal death kinetics, the activation energy (Ea, kJ·mol(-1)) and the activation volume (Va, mL·mol(-1)) values were computed further to describe the temperature and pressure dependencies of the scale parameter (δ, min), respectively. A higher δ value was obtained for each microbe at a lower temperature and it decreased with an increase in pressure. A secondary kinetic model was developed describing the inactivation rate (k, min(-1)) as a function of pressure (P, MPa) and temperature (T, K) including the dependencies of Ea and Va on P and T, respectively.

Dynamic modeling the composting process of the mixture of poultry manure and wheat straw.

Due to lack of understanding of the complex nature of the composting process, there is a need to provide a valuable tool that can help to improve the prediction of the process performance but also its optimization. Therefore, the main objective of this study is to develop a comprehensive mathematical model of the composting process based on microbial kinetics. The model incorporates two different microbial populations that metabolize the organic matter in two different substrates. The model was validated by comparison of the model and experimental data obtained from the composting process of the mixture of poultry manure and wheat straw. Comparison of simulation results and experimental data for five dynamic state variables (organic matter conversion, oxygen concentration, carbon dioxide concentration, substrate temperature and moisture content) showed that the model has very good predictions of the process performance. According to simulation results, the optimum values for air flow rate and ambient air temperature are 0.43 l min(-1) kg(-1)OM and 28 °C, respectively. On the basis of sensitivity analysis, the maximum organic matter conversion is the most sensitive among the three objective functions. Among the twelve examined parameters, µmax,1 is the most influencing parameter and X1 is the least influencing parameter.

Whole cell kinetics of ureolysis by Sporosarcina pasteurii.

AIMS: Ureolysis drives microbially induced calcium carbonate precipitation (MICP). MICP models typically employ simplified urea hydrolysis kinetics that do not account for cell density, pH effect or product inhibition. Here, ureolysis rate studies with whole cells of Sporosarcina pasteurii aimed to determine the relationship between ureolysis rate and concentrations of (i) urea, (ii) cells, (iii) NH4+ and (iv) pH (H(+) activity). METHODS AND RESULTS: Batch ureolysis rate experiments were performed with suspended cells of S. pasteurii and one parameter was varied in each set of experiments. A Michaelis-Menten model for urea dependence was fitted to the rate data (R(2)  = 0·95) using a nonlinear mixed effects statistical model. The resulting half-saturation coefficient, Km , was 305 mmol l(-1) and maximum rate constant, Vmax , was 200 mmol l(-1)  h(-1) . However, a first-order model with k1  = 0·35 h(-1) fit the data better (R(2)  = 0·99) for urea concentrations up to 330 mmol l(-1) . Cell concentrations in the range tested (1 × 10(7) -2 × 10(8)  CFU ml(-1) ) were linearly correlated with ureolysis rate (cell dependent Vmax' = 6·4 × 10(-9)  mmol CFU(-1)  h(-1) ). CONCLUSIONS: Neither pH (6-9) nor ammonium concentrations up to 0·19 mol l(-1)  had significant effects on the ureolysis rate and are not necessary in kinetic modelling of ureolysis. Thus, we conclude that first-order kinetics with respect to urea and cell concentrations are likely sufficient to describe urea hydrolysis rates at most relevant concentrations. SIGNIFICANCE AND IMPACT OF THE STUDY: These results can be used in simulations of ureolysis driven processes such as microbially induced mineral precipitation and they verify that under the stated conditions, a simplified first-order rate for ureolysis can be employed. The study shows that the kinetic models developed for enzyme kinetics of urease do not apply to whole cells of S. pasteurii.

Kinetic parameter estimation in N. europaea biofilms using a 2-D reactive transport model.

Biofilms of the ammonia oxidizing bacterium Nitrosomonas europaea were cultivated to study microbial processes associated with ammonia oxidation in pure culture. We explored the hypothesis that the kinetic parameters of ammonia oxidation in N. europaea biofilms were in the range of those determined with batch suspended cells. Oxygen and pH microelectrodes were used to measure dissolved oxygen (DO) concentrations and pH above and inside biofilms and reactive transport modeling was performed to simulate the measured DO and pH profiles. A two dimensional (2-D) model was used to simulate advection parallel to the biofilm surface and diffusion through the overlying fluid while reaction and diffusion were simulated in the biofilm. Three experimental studies of microsensor measurements were performed with biofilms: i) NH3 concentrations near the Ksn value of 40 µM determined in suspended cell tests ii) Limited buffering capacity which resulted in a pH gradient within the biofilms and iii) NH3 concentrations well below the Ksn value. Very good fits to the DO concentration profiles both in the fluid above and in the biofilms were achieved using the 2-D model. The modeling study revealed that the half-saturation coefficient for NH3 in N. europaea biofilms was close to the value measured in suspended cells. However, the third study of biofilms with low availability of NH3 deviated from the model prediction. The model also predicted shifts in the DO profiles and the gradient in pH that resulted for the case of limited buffering capacity. The results illustrate the importance of incorporating both key transport and chemical processes in a biofilm reactive transport model.

Effect of Nisin's Controlled Release on Microbial Growth as Modeled for Micrococcus luteus.

The need for safe food products has motivated food scientists and industry to find novel technologies for antimicrobial delivery for improving food safety and quality. Controlled release packaging is a novel technology that uses the package to deliver antimicrobials in a controlled manner and sustain antimicrobial stress on the targeted microorganism over the required shelf life. This work studied the effect of controlled release of nisin to inhibit growth of Micrococcus luteus (a model microorganism) using a computerized syringe pump system to mimic the release of nisin from packaging films which was characterized by an initially fast rate and a slower rate as time progressed. The results show that controlled release of nisin was strikingly more effective than instantly added ("formulated") nisin. While instant addition experiments achieved microbial inhibition only at the beginning, controlled release experiments achieved complete microbial inhibition for a longer time, even when as little as 15% of the amount of nisin was used as compared to instant addition.