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.

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.

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.

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.

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.

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.

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.

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 β-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.

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.

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.