Modeling of bacterial growth as a function of temperature

Metagenomic Thermometer

Various microorganisms exist in environments, and each of them has its optimal growth temperature (OGT). The relationship between genomic information and OGT of each species has long been studied, and one such study revealed that OGT of prokaryotes can be accurately predicted based on the fraction of seven amino acids (IVYWREL) among all encoded amino-acid sequences in its genome. Extending this discovery, we developed a 'Metagenomic Thermometer' as a means of predicting environmental temperature based on metagenomic sequences. Temperature prediction of diverse environments using publicly available metagenomic data revealed that the Metagenomic Thermometer can predict environmental temperatures with small temperature changes and little influx of microorganisms from other environments. The accuracy of the Metagenomic Thermometer was also confirmed by a demonstration experiment using an artificial hot water canal. The Metagenomic Thermometer was also applied to human gut metagenomic samples, yielding a reasonably accurate value for human body temperature. The result further suggests that deep body temperature determines the dominant lineage of the gut community. Metagenomic Thermometer provides a new insight into temperature-driven community assembly based on amino-acid composition rather than microbial taxa.

Thermal adaptation of soil microbial growth traits in response to chronic warming

IMPORTANCE

Soils are the largest terrestrial carbon sink and the foundation of our food, fiber, and fuel systems. Healthy soils are carbon sinks, storing more carbon than they release. This reduces the amount of carbon dioxide released into the atmosphere and buffers against climate change. Soil microbes drive biogeochemical cycling and contribute to soil health through organic matter breakdown, plant growth promotion, and nutrient distribution. In this study, we determined how soil microbial growth traits respond to long-term soil warming. We found that bacterial isolates from warmed plots showed evidence of adaptation of optimum growth temperature. This suggests that increased microbial biomass and growth in a warming world could result in greater carbon storage. As temperatures increase, greater microbial activity may help reduce the soil carbon feedback loop. Our results provide insight on how atmospheric carbon cycling and soil health may respond in a warming world.

Predictive model for growth of Pseudomonas spp. on fresh duck breast as a function of temperature

This study was performed to develop a predictive growth model of Pseudomonas spp. to ensure the safety of fresh duck breast. Sterile fresh duck breasts were inoculated with Pseudomonas spp. and stored at 4°C, 10°C, 15°C, 20°C, 25°C, and 30°C to measure the microbial change. The Baranyi primary model was used to simulate the growth changes of Pseudomonas spp. at different temperatures. The square root type model and hyperbolic function as secondary models were used to model the effect of temperature on the maximum specific growth rate and lag phase duration. The results showed that the primary models fitted the growth data well (all R2 > 0.900 and RMSE were close to 0). In validation study of secondary model, R2 were 0.987 and 0.925, RMSE were 0.017 and 1.825, respectively, indicating that the parameters of primary models were accurately predicted by secondary models. The validation experiments at tested temperatures proved that the changes of Pseudomonas spp. could be predicted accurately by the developed models combining primary and secondary models both at constant and variable temperatures. The model could be applied to predict the growth of Pseudomonas spp. in logistics for avoiding microbial spoilage on fresh duck breast.

Mathematical modeling of dark fermentative hydrogen and soluble by-products generations from water hyacinth

The production of hydrogen and soluble metabolite products from water hyacinth via dark fermentation was modeled. The model was built on the assumption that the substrate exists in two forms (i.e., soluble and particulate) and undergoes two stages (i.e., hydrolysis and acidogenesis) in the dark fermentation process. The modified Michaelis-Menten and surface-limiting models were applied to describe the hydrolysis of soluble and particulate forms, respectively. Meanwhile, the acidogenesis stage was modeled based on the multi-substrate-single-biomass model. The effects of temperature, pH, and substrate concentration were integrated into the model to increase flexibility. As a result, the model prediction agreed with the experimental and literature data of water hyacinth-fed dark fermentation, with high coefficient of determination values of 0.92 - 0.97 for hydrogen and total soluble metabolite products. These results indicate that the proposed model could be further applied to dark fermentation's downstream and hybrid processes using water hyacinth and other substrates.

In Search of Proximate Triggers of Anthrax Outbreaks in Wildlife: A Hypothetical Individual-Based Model of Plasmid Transfer within Bacillus Communities

Bacillus anthracis, the causative agent of anthrax in humans, livestock, and wildlife, exists in a community with hundreds of other species of bacteria in the environment. Work on the genetics of these communities has shown that B. anthracis shares a high percentage of chromosomal genes with both B. thuringiensis and B. cereus, and that phenotypic differences among these bacteria can result from extra-chromosomal DNA in the form of plasmids. We developed a simple hypothetical individual-based model to simulate the likelihood of detecting plasmids with genes encoding anthrax toxins within bacterial communities composed of B. anthracis, B. thuringiensis, and B. cereus, and the surrounding matrix of extra-cellular polymeric substances. Simulation results suggest the horizontal transfer of plasmids with genes encoding anthrax toxins among Bacillus species persisting outside the host could function as a proximate factor triggering anthrax outbreaks.

Treatment of secondary urban wastewater with a low ammonium-tolerant marine microalga using zeolite-based adsorption

The low tolerance of marine microalgae to ammonium and hyposalinity limits their use in urban wastewater (UWW) treatments. In this study, using the marine microalga Amphidinium carterae, it is demonstrated for the first time that this obstacle can be overcome by introducing a zeolite-based adsorption step to obtain a tolerable UWW stream. The maximum ammonium adsorption capacities measured in the natural zeolite used are among the highest reported. The microalga grows satisfactorily in mixtures of zeolite-treated UWW and seawater at a wide range of proportions, both with and without adjusting the salinity, as long as the ammonium concentration is below the threshold tolerated by the microalgae (6.3 mg L^-1). A proof of concept performed in 10-L bubble column photobioreactors with different culture strategies, including medium recycling, showed an enhanced biomass yield relative to a control with no UWW. No noticeable effect was observed on the production of specialty metabolites.

Modeling of Nitrification Kinetics in a Respirometric Biosensor under Suboptimal Conditions

Sensitive detection with cell biosensors requires optimization of their working conditions and standardization of the response in variable physicochemical conditions. The introduction of an analyte to a sensor, which contributes to this variability, may account for the modeling of microbial metabolism. We constructed a multiparameter model of a water toxicity sensor of Automatic Biodetector for Water Toxicity (ABTOW), developed by our group and based on nitrifying bacteria. The model describes the kinetics of nitrification as a function of four orthogonal parameters: temperature, pH, oxygen and ammonium concentration. Furthermore, we characterized the signal-to-noise ratio (SNR) of the ABTOW readout as a function of these parameters. Thus, a region of parameter space corresponding to optimal ABTOW operation is identified and its sensitivity quantified. We applied the model to describe the ABTOW performance in non-equilibrium conditions produced by rapid changes in pH and temperature. In sum, the model based on four physicochemical parameters describes changes in the biosensor’s activity, the biological element of which are nitrifying bacteria characterized by simple chemolithoautotrophic metabolism. The description of reaction kinetics through multiparameter modeling in combination with stability analysis can find application in process control in biotechnology, biodetection and environmental research.

Development of a Monte Carlo simulation model to predict pasteurized fluid milk spoilage due to post-pasteurization contamination with gram-negative bacteria

Psychrotolerant gram-negative bacteria introduced as post-pasteurization contamination (PPC) are a major cause of spoilage and reduced shelf life of high-temperature, short-time pasteurized fluid milk. To provide improved tools to (1) predict pasteurized fluid milk shelf life as influenced by PPC and (2) assess the effectiveness of different potential interventions that could reduce spoilage due to PPC, we developed a Monte Carlo simulation model that predicts fluid milk spoilage due to psychrotolerant gram-negative bacteria introduced as PPC. As a first step, 17 gram-negative bacterial isolates frequently associated with fluid milk spoilage were selected and used to generate growth data in skim milk broth at 6°C. The resulting growth parameters, frequency of isolation for the 17 different isolates, and initial concentration of bacteria in milk with PPC, were used to develop a Monte Carlo model to predict bacterial number at different days of shelf life based on storage temperature of milk. This model was then validated with data from d 7 and 10 of shelf life, collected from commercial operations. The validated model predicted that the parameters (1) maximum growth rate and (2) storage temperature had the greatest influence on the percentage of containers exceeding 20,000 cfu/mL standard plate count on d 7 and 10 (i.e., spoiling due to PPC), which indicates that accurate data on maximum growth rate and storage temperature are important for accurate predictions. In addition to allowing for prediction of fluid milk shelf life, the model allows for simulation of "what-if" scenarios, which allowed us to predict the effectiveness of different interventions to reduce overall fluid milk spoilage due to PPC through a set of proof-of-concept scenario (e.g., frequency of PPC in containers reduced from 100% to 10%; limiting distribution temperature to a maximum of 6°C). Combined with other models, such as previous models on fluid milk spoilage due to psychrotolerant spore-forming bacteria, the data and tools developed here will allow for rational, digitally enabled, fluid milk shelf life prediction and quality enhancement.

Mathematical Modeling of Total Volatile Basic Nitrogen and Microbial Biomass in Stored Rohu (Labeo rohita) Fish

The paper deals with the dynamical behavior of fish volatiles and microbial growth in stored Rohu fish through mathematical modeling. Total volatile basic nitrogen (TVB-N) is formed in stored Rohu (Labeo rohita) fish due to some complicated biochemical activities. It considered the biomass populace of volatiles (TVB-N) and microorganisms in fish stored at two different temperatures, separately. The different models may be used to forecast TVB-N, microbial populace (total viable count; TVC), and various properties change during nourishment stockpiling coordination and diverse preparing tasks. Models might be dynamic, exact, hypothetical, and stochastic in nature. Various parameters are required to build up a model which can be utilized to foresee the freshness and timeframe of realistic usability of storage duration. The ecosystem is represented by algebraic equations involving volatile compounds and microbial populations separately. TVB-N and TVC of stored rohu fish was determined at an interval of 4 days for 24 days. The initial and final biomass of TVB-N was 4.57 (fresh sample), 19.88 (24 days at 5°C), and 7.10 mg/100 g (24th day at 0°C), respectively. The TVC values were found to be 2.29 (fresh sample), 9.5 (24 days at 5°C) and 8.1 log (cfu/g) (24 days at 0°C). Exponential, modified exponential, Howgate, and adapted Howgate models were considered for modeling the TVB-N formation, whereas logistic, modified logistic, Gompertz, and modified Gompertz model were taken forward for modeling the microbial biomass developed in stored rohu fish. The exponential model found be the best fit model fit model for TVB-N prediction in rohu fish stored at 0 and 5°C as it showed the highest R2 (0.9796, 0.9887) the lowest χ2 (0.2782, 0.3976), RMSE (0.52741, 0.6306) AIC (−7.3122, −4.8106), AICc (−0.5122, 1.9894) and BIC (−7.4204, −4.9188), respectively. The Gompertz model was found to be the best fit model for microbial biomass prediction in rohu fish stored at 5°C (R2 = 0.9947, χ2 = 0.0537, AIC = −18.379, AICc = −6.3792 and BIC = −18.542), in contrast, both of the logistic and modified logistic models were the best suited at 0°C storage condition (R2 = 0.9919, χ2 = 0.0823).

Predictive model for growth of Clostridium botulinum from spores during cooling of cooked ground chicken

Cooking temperature of poultry meat is typically inadequate to inactivate the heat resistant spores of Clostridium botulinum. The purpose of this study is to develop a predictive model for C. botulinum during cooling of cooked ground chicken. Cooked chicken was inoculated with a cocktail of five strains of proteolytic C. botulinum type A and five strains of proteolytic C. botulinum type B to yield a final spore concentration of approximately 2 log CFU/g. The growth of C. botulinum was determined at constant temperatures from 10 to 46 °C. Dynamic temperature experiments were performed with continued cooling from 54.4 to 4.4 °C or 7.2 °C in mono- or bi-phasic cooling profiles, respectively. The Baranyi primary model was used to fit growth data and the modified Ratkowsky secondary model was used to fit growth rates with respect to temperature. The primary models fitted the growth data well (R² values ranging from 0.811 to 0.988). The R² and root mean square error (RMSE) of the modified Ratkowsky secondary model were 0.95 and 0.06, respectively. Out of 11 prediction error values calculated in this study, ten were within the limit of acceptable prediction zone (-1.0 to 0.5), indicating a good fit of the model. The predictive model will assist institutional food service operations in determining the safety of cooked ground chicken subjected to different cooling periods.

Mathematical modeling of nutritional, color, texture, and microbial activity changes in fruit and vegetables during drying: A critical review

Retention of quality attributes during drying of fruit and vegetables is a prime concern since the product's acceptability depends on the overall quality; particularly on the nutritional, color, and physical attributes. However, these quality parameters deteriorate during drying. Food quality changes are strongly related to the drying conditions and researchers have attempted to develop mathematical models to understand these relationships. A better insight toward the degradation of quality attributes is crucial for making real predictions and minimizing the quality deterioration. The previous empirical quality models employed kinetic modeling approaches to describe the quality changes and therefore, lack the realistic understanding of fundamental transport mechanisms. In order to develop a physics based mathematical model for the prediction of quality changes during drying, an in-depth understanding of research progress made toward this direction is indispensable. Therefore, the main goal of this paper is to present a critical review of the mathematical models developed and applied to describe the degradation kinetics of nutritional, color, and texture attributes during drying of fruit and vegetables and microbial growth model during storage. This review also presents the advantages and drawbacks of the existing models along with their industrial relevance. Finally, future research propositions toward developing physics-based mathematical model are presented.

Generalized temperature dependence model for anammox process kinetics

Temperature is a key operational factor influencing the anammox process kinetics. In particular, at temperatures below 15 °C, the specific anammox activity (SAA) considerably decreases. This study aimed to describe the temperature dependence of the anammox process kinetics in the temperature range from 10 to 55 °C, including the specific characteristics of "cold anammox". The commonly used Arrhenius and extended and modified Ratkowsky equations were examined. The Ratkowsky equations yielded a strong correlation (coefficient of determination, R² = 0.93-0.96) between the measured and predicted data over the analyzed temperature range (10-55 °C). However, these equations could not correctly reflect the anammox temperature dependence at temperatures below 15 °C (R² = 0.36-0.48). Therefore, a new generalized temperature model was proposed. The generalized temperature equation (GTE) considered the division of the analyzed temperature range into three temperature ranges: 10-15 °C, 15-35 °C and 35-55 °C. The ranges correspond to "cold anammox", "(low) mesophilic anammox" and "thermophilic anammox". The applied approach yielded a strong correlation between the measured and predicted SAA (R² = 0.97) over the temperature range from 10 to 55 °C and over the low-temperature range from 10 to 15 °C (R² = 0.99). Overall, the GTE could enhance the predictions of the temperature dependence of the anammox process kinetics. The GTE can help examine anammox-based bioaugmentation systems operating at both high temperatures (sidestream reactors) and low temperatures (mainstream reactors).

Dependence of bacterial growth rate on dynamic temperature changes

Temperature is an important determinant of bacterial growth. While the dependence of bacterial growth on different temperatures has been well studied for many bacterial species, prediction of bacterial growth rate for dynamic temperature changes is relatively unclear. Here, the authors address this issue using a combination of experimental measurements of the growth, at the resolution of 5 min, of Escherichia coli and mathematical models. They measure growth curves at different temperatures and estimate model parameters to predict bacterial growth profiles subject to dynamic temperature changes. They compared these predicted growth profiles for various step‐like temperature changes with experimental measurements using the coefficient of determination and mean square error and based on this comparison, ranked the different growth models, finding that the generalised logistic growth model gave the smallest error. They note that as the maximum specific growth increases the duration of this growth predominantly decreases. These results provide a basis to compute the dependence of the growth rate parameter in biomolecular circuits on dynamic temperatures and may be useful for designing biomolecular circuits that are robust to temperature.

Modelling the growth of Staphylococcus aureus on cooked broccoli under isothermal conditions

In this study, we developed predictive models describing the growth of Staphylococcus aureus on cooked broccoli florets. A pool of 3.5 log CFU/g of five S. aureus strains were inoculated on 10 g broccoli portions. The samples were then stored at 10, 20, 30 and 37 °C, and colonies were enumerated at different time intervals. Baranyi and Roberts model was fitted to the data using a Bayesian Adaptive Markov Chain Monte Carlo for estimation of the growth parameters. S. aureus showed low growth at 10 °C on broccoli samples and at 20-37 °C interval, Baranyi and Roberts model fitted well to the experimental data (R²>0.97). Estimated growth parameters were correlated with the possibility of toxin production and indicate the potential presence of these biological hazards on contaminated broccoli after heat treatment. Additionally, linear regression was performed for growth rate as storage temperature function. This secondary model followed a linear tendency with R²=0.997 and was compared with two tertiary models (ComBase Predictor and Pathogen Modeling Program) and literature data, demonstrating similar growth rate values of both. These results can be helpful for food services and managers to establish food safety standards for S. aureus growth on cooked broccoli.

Growth rate-dependent synthesis of halomethanes in marine heterotrophic bacteria and its implications for the ozone layer recovery

Halomethanes (e.g., CH₃ Cl, CH₃ Br, CH₃ I and CHBr₃ ) are ozone-depleting compounds that, in contrast to the human-made chlorofluorocarbons, marine organisms synthesize naturally. Therefore, their production cannot be totally controlled by human action. However, identifying all their natural sources and understanding their synthesis regulation can help to predict their production rates and their impact on the future recovery of the Earth's ozone layer. Here we show that the synthesis of mono-halogenated halocarbons CH₃ Cl, CH₃ Br, and CH₃ I is a generalized process in representatives of the major marine heterotrophic bacteria groups. Furthermore, halomethane production was growth rate dependent in all the strains we studied, implying uniform synthesis regulation patterns among bacterioplankton. Using these experimental observations and in situ halomethane concentrations, we further evaluated the potential production rates associated with higher bacterial growth rates in response to global warming in a coastal environment within the Southern California Bight. Our estimates show that a 3°C temperature rise would translate into a 35%-84% increase in halomethane production rate by 2100. Overall, these data suggest that marine heterotrophic bacteria are significant producers of these climate-relevant gases and that their contribution to the atmospheric halogen budget could increase in the future, impacting the ozone layer recovery.

Effect of salt and temperature on the growth of Escherichia coli PSII

Presence of pathogenic strains of Escherichia coli in foodstuffs may pose a health risk for a consumer. Therefore, knowledge on the effect of environmental factors on the growth ability of E. coli is of great importance. In this work, the effect of incubation temperature (6–46 °C) and the combined effect of temperature and water activity (0.991–0.930) on the growth dynamic of E. coli PSII were analysed. Based on the growth curves obtained, growth parameters were calculated by using the Baranyi D-model. Growth parameters were further analysed in secondary phase of predictive modelling. Using the CM model that describes the effect of combined factors, cardinal values (Tmin = 4.8 ± 0.4 °C, Topt = 41.1 ± 0.8 °C, Tmax = 48.3 ± 0.9 °C, awmin = 0.932 ± 0.001, and awopt = 0.997 ± 0.003) for the isolate were calculated. Under optimal conditions, the specific growth rate is µopt = 2.84 ± 0.08 h−1. The results obtained may contribute to the assessment of the risk associated with the possible E. coli presence in raw materials and to the search for preventive measures with defined degree of accuracy and reliability.

Single Mathematical Parameter for Evaluation of the Microorganisms' Growth as the Objective Function in the Optimization by the DOE Techniques

The cultivation of bacteria sets a ground for studying biological processes in many scientific disciplines. The development of the bacterial population is commonly described with three factors that can be used to evaluate culture conditions. However, selecting only one of them for the optimization protocol is rather problematic and may lead to unintended errors. Therefore, we proposed a novel mathematical approach to obtain a single factor that could be used as the objective function to evaluate the whole growth dynamic and support the optimization of the biomass production process. The sigmoidal-shape curve, which is the commonly used function to plot the amount of biomass versus time, was the base for the mathematical analysis. The key process parameters, such as maximal specific growth rate and lag-phase duration were established with the use of mathematical coefficients of the model curve and combined to create the single growth parameter. Moreover, this parameter was used for the exemplary optimization of the cultivation conditions of Klebsiella pneumoniae that was cultured to be further used in the production of lytic bacteriophages. The proposed growth parameter was successfully validated and used to calculate the optimal process temperature of the selected bacterial strain. The obtained results indicated that the proposed mathematical approach could be effortlessly adapted for a precise evaluation of growth curves.

A predictive growth model for Clostridium botulinum during cooling of cooked uncured ground beef

A dynamic model to predict the germination and outgrowth of Clostridium botulinum spores in cooked ground beef was presented. Raw ground beef was inoculated with a ten-strain C. botulinum spore cocktail to achieve approximately 2 log spores/g. The inoculated ground beef was vacuum packaged, cooked to 71 °C to heat shock the spores, cooled to below 10 °C, and incubated isothermally at temperatures from 10 to 46 °C. C. botulinum growth was quantified and fitted into the primary Baranyi Model. Secondary models were fitted to maximum specific growth rate and lag phase duration using Modified Ratkowsky equation (R² 0.96) and hyperbolic function (R² 0.94), respectively. Similar experiments were also performed under non-isothermal (cooling) conditions. Acceptable zone prediction (APZ) analysis was conducted on growth data collected over 3 linear cooling regimes from the current study. The model performance (prediction errors) for all 22 validation data points collected in the current work were within the APZ limits (-1.0 to +0.5 log CFU/g). Additionally, two other growth data sets of C. botulinum reported in the literature were also subjected to the APZ analysis. In these validations, 20/22 and 10/14 predictions fell within the APZ limits. The model presented in this work can be employed to predict C. botulinum spore germination and growth in cooked uncured beef under non-isothermal conditions. The beef industry processors and food service organizations can utilize this predictive microbial model for cooling deviations and temperature abused situations and in developing customized process schedules for cooked, uncured beef products.

Potential Cultivation of Lactobacillus pentosus from Human Breastmilk with Rapid Monitoring through the Spectrophotometer Method

The present study focused on the development of a new method to determine the lag phase of Lactobacillus in breast milk which was attained during the 1st, 3rd, and 6th month (M1, M3, and M6). The colonies’ phylogenetic analysis, derived from the 16S rRNA gene sequences, was evaluated with genus Lactobacillus pentosus and achieved a similarity value of 99%. Raman spectroscopy in optical densities of 600 nm (OD600) were used for six consecutive days to observe the changes of the cell growth rate. The values of OD600 were well fitted with the regression model. From this work, M1 was found to be the longest lag phase in 18 h, and it was 17% to 27% longer compared to M3 and M6, respectively. However, the samples of M3 and M6 showed the shortest duration in reaching 0.5 of OD600 nm (16 h) which was enhanced by 80% and 96% compared to M1, respectively. These studies will be of significance when applied in determining the bacteria growth curve and in assessing the growth behavior for the strain in human breast milk.

Growth, Cell Division, and Gene Expression of Escherichia coli at Elevated Concentrations of Magnesium Sulfate: Implications for Habitability of Europa and Mars

We perform quantitative studies of the growth, death, and gene expression of Escherichia coli in a wide range of magnesium sulfate (MgSO 4 ) concentrations (0-2.5 M). Elevated concentration of MgSO 4 causes the inhibition of cell growth, leading to an increase in the population doubling time. We find that cells exhibit three distinct morphological phenotypes-(i) normal, (ii) filamentous, and (iii) small cells at 1 . 25 M MgSO 4 . Filamentous cells arise due to the lack of cell division, while the small cells arise due to the partial plasmolysis of the cells. We further find that cell death starts for salt concentrations >1.25 M and increases with an increasing concentration of MgSO 4 . For salt concentrations ≥1.66 M, the growth of cells stops and all the cells become smaller than the control cells, suggesting the plasmolysis of the population. Cells grown at salt concentration up to 2 . 07 M are reversible in both the growth rate and morphology upon the removal of the salt stress. The time scale of reversibility increases with increasing salt concentration. Finally, we investigate the expression of an osmotically inducible gene (osmC), genes involved in magnesium transport (corA), sulfate transport (cysP), and osmotically driven transport of water (aqpZ). We find that a high concentration of magnesium sulfate leads to the upregulation of cysP and osmC.

Compounding Effects of Climate Warming and Antibiotic Resistance

Bacteria have evolved diverse mechanisms to survive environments with antibiotics. Temperature is both a key factor that affects the survival of bacteria in the presence of antibiotics and an environmental trait that is drastically increasing due to climate change. Therefore, it is timely and important to understand links between temperature changes and selection of antibiotic resistance. This review examines these links by synthesizing results from laboratories, hospitals, and environmental studies. First, we describe the transient physiological responses to temperature that alter cellular behavior and lead to antibiotic tolerance and persistence. Second, we focus on the link between thermal stress and the evolution and maintenance of antibiotic resistance mutations. Finally, we explore how local and global changes in temperature are associated with increases in antibiotic resistance and its spread. We suggest that a multidisciplinary, multiscale approach is critical to fully understand how temperature changes are contributing to the antibiotic crisis.

Growth of Staphylococcus aureus 2064 described by predictive microbiology: From primary to secondary models

The growth of Staphylococcus aureus 2064 isolate in model nutrient broth was studied as affected by temperature and water activity using principles and models of predictive microbiology. Specific rates resulting from growth curves fitted by the Baranyi model were modelled by the secondary Ratkowsky model for suboptimal temperature range (RTKsub) as well as the Ratkowsky extended model (RTKext) and cardinal model (CM) in the whole temperature range. With the biological background of the RTKext model, cardinal values of temperature T min = 6.06 °C and T max = 47.9 °C and water activity aw min = 0.859 were calculated and validated with cardinal values estimated by CM (T min = 7.72 °C, T max = 46.73 °C, aw min = 0.808). CM also provided other cardinal values, T opt = 40.63 °C, aw opt = 0.994, as well as optimal specific growth rate of 1.97 h–1 (at T opt and aw opt). To evaluate the goodness of fit of all models, mathematical and graphical validation was performed and the statistical indices proved appropriateness of all the secondary models used.

Modeling and Exploiting Microbial Temperature Response

Temperature is an important parameter in bioprocesses, influencing the structure and functionality of almost every biomolecule, as well as affecting metabolic reaction rates. In industrial biotechnology, the temperature is usually tightly controlled at an optimum value. Smart variation of the temperature to optimize the performance of a bioprocess brings about multiple complex and interconnected metabolic changes and is so far only rarely applied. Mathematical descriptions and models facilitate a reduction in complexity, as well as an understanding, of these interconnections. Starting in the 19th century with the “primal” temperature model of Svante Arrhenius, a variety of models have evolved over time to describe growth and enzymatic reaction rates as functions of temperature. Data-driven empirical approaches, as well as complex mechanistic models based on thermodynamic knowledge of biomolecular behavior at different temperatures, have been developed. Even though underlying biological mechanisms and mathematical models have been well-described, temperature as a control variable is only scarcely applied in bioprocess engineering, and as a conclusion, an exploitation strategy merging both in context has not yet been established. In this review, the most important models for physiological, biochemical, and physical properties governed by temperature are presented and discussed, along with application perspectives. As such, this review provides a toolset for future exploitation perspectives of temperature in bioprocess engineering.

Predictive Model of Listeria monocytogenes Growth in Queso Fresco

Listeria monocytogenes is a hardy psychrotrophic pathogen that has been linked to several cheese-related outbreaks in the United States, including a recent outbreak in which a fresh cheese (queso fresco) was implicated. The purpose of this study was to develop primary, secondary, and tertiary predictive models for the growth of L. monocytogenes in queso fresco and to validate these models using nonisothermal time and temperature profiles. A mixture of five strains of L. monocytogenes was used to inoculate pasteurized whole milk to prepare queso fresco. Ten grams of each fresh cheese sample was vacuum packaged and stored at 4, 10, 15, 20, 25, and 30°C. From samples at each storage temperature, subsamples were removed at various times and diluted in 0.1% peptone water, and bacteria were enumerated on Listeria selective agar. Growth data from each temperature were fitted using the Baranyi model as the primary model and the Ratkowsky model as the secondary model. Models were then validated using nonisothermal conditions. The Baranyi model was fitted to the isothermal growth data with acceptable goodness of fit statistics (R² = 0.928; root mean square error = 0.317). The Ratkowsky square root model was fitted to the specific growth rates at different temperatures (R² = 0.975). The tertiary model developed from these models was validated using the growth data with two nonisothermal time and temperature profiles (4 to 20°C for 19 days and 15 to 30°C for 11 days). Data for these two profiles were compared with the model prediction using an acceptable prediction zone analysis; >70% of the growth observations were within the acceptable prediction zone (between -1.0 and 0.5 log CFU/g). The model developed in this study will be useful for estimating the growth of L. monocytogenes in queso fresco. These predictions will help in estimation of the risk of listeriosis from queso fresco under extended storage and temperature abuse conditions.

Dataset on the use of the Ratkowsky model for describing the influence of storage temperature on microbial growth in hake fillets (Merluccius merluccius) stored under MAP

This article presents the results obtained after applying the Ratkowsky model for developing secondary models describing the influence of storage temperature on microbial growth in hake fillets packaged under a modified atmosphere (MAP) rich in CO₂ (50% CO₂/50% N₂). For this purpose the growth parameters (λ, μ_max) already calculated in the related article "Modelling microbial growth in Modified-Atmosphere-Packed hake (Merluccius merluccius) fillets stored at different temperatures" [1] were used. The data include the fit and goodness of the fit parameters calculated as well as the comparison between fitted and observed data.

Effects of thermal boundary condition on methane oxidation in landfill cover soil at different ambient temperatures

Microbial aerobic methane oxidation (MAMO) has been considered as an environmental-friendly method for mitigating methane emission from municipal landfill sites. Soil column has in a landfill cover under one-dimensional (1-D) condition. However, most of the published soil column tests failed to simulate 1-D heat transfer due to the use of thermal conductive boundary at the sidewall. In the present study, a heavily instrumented soil column was developed to quantify the effects of thermal boundary condition on the methane oxidation efficiency under different ambient temperatures in landfill cover soil. The sidewall of the soil column was thermally insulated to ensure 1-D heat transport as would have been typically expected in the field condition. Two soil column tests with and without thermal insulation were conducted at a range of controlled ambient temperatures from 15 to 30°C, for studying how soil moisture, matric suction, gas pressure, soil temperature and gas concentration evolve with MAMO. The test results reveal that ignoring thermal insulation in a soil column test would result in a greater loss of soil heat generation by MAMO and hence oxidation efficiency by up to 100% for the range of temperature considered. When the ambient temperature increased to 30°C (but less than the optimum temperature for MAMO), the MAMO efficiency increased abruptly at first but then decreased substantially with time, and this is likely due to the accumulation of biomass generated by MAMO.

Evaluating temperature-induced regulation of a ROSE-like RNA-thermometer for heterologous rhamnolipid production in Pseudomonas putida KT2440

The microbial production of rhamnolipids has been in the focus of research for the last decades. Today, mainly heterologous production systems are targeted due to the advantage of non-pathogenic hosts as well as uncoupling from complex quorum sensing regulatory networks compared to their natural producer Pseudomonas aeruginosa. In the recent past, the presence and function of a ROSE-like RNA-thermometer located in the 5′UTR of the rhamnosyltransferase genes rhlAB has been reported in wild type P. aeruginosa. In this study, the temperature-induced regulation of this native RNA-thermometer for heterologous rhamnolipid production was evaluated and its potential application for process control is discussed. For this purpose, the non-pathogenic production host P. putida KT2440 containing the rhlAB genes with the native P. aeruginosa 5′-UTR region was used. The system was evaluated and characterized regarding the effect of temperature on growth and product formation, as represented by efficiency parameters and yields. Experimental data suggests a major effect of temperature on specific rhamnolipid production rates. With maximum values of 0.23 g/(g h) at 37 °C, this constitutes a more than 60% increase compared to the production rate of 0.14 g/(g h) at the growth optimum of 30 °C. Interestingly however, control experiments unveiled that besides the regulatory effect of the RNA-thermometer, multiple metabolic effects may contribute equally to the observed increase in production rate. As such, this work constitutes an important step towards the utilization of temperature-based process designs and enables the possibility for novel approaches for process control.

Challenges of biofilm control and utilization: lessons from mathematical modelling

This article reviews modern applications of mathematical descriptions of biofilm formation. The focus is on theoretically obtained results which have implications for areas including the medical sector, food industry and wastewater treatment. Examples are given as to how models have contributed to the overall knowledge on biofilms and how they are used to predict biofilm behaviour. We conclude that the use of mathematical models of biofilms has demonstrated over the years the ability to significantly contribute to the vast field of biofilm research. Among other things, they have been used to test various hypotheses on the nature of interspecies interactions, viability of biofilm treatment methods or forces behind observed biofilm pattern formations. Mathematical models can also play a key role in future biofilm research. Many models nowadays are analysed through computer simulations and continue to improve along with computational capabilities. We predict that models will keep on providing answers to important challenges involving biofilm formation. However, further strengthening of the ties between various disciplines is necessary to fully use the tools of collective knowledge in tackling the biofilm phenomenon.

Characterization and Control of Erwinia spp. and Pluralibacter sp. in Tuna Salad Preparations

HIGHLIGHTS

Early detection of bacteria using SPR imaging and event counting: experiments with Listeria monocytogenes and Listeria innocua

Foodborne pathogens are of significant concern in the agrifood industry and the development of associated rapid detection and identification methods are of major importance. This paper describes the novel use of resolution-optimized prism-based surface plasmon resonance imaging (RO-SPRI) and data processing for the detection of the foodborne pathogens Listeria monocytogenes and Listeria innocua. With an imaging spatial resolution on the order of individual bacteria (2.7 ± 0.5 μm × 7.9 ± 0.6 μm) over a field of view 1.5 mm2, the RO-SPRI system enabled accurate counting of individual bacteria on the sensor surface. Using this system, we demonstrate the detection of two species of Listeria at an initial concentration of 2 × 102 CFU mL-1 in less than 7 hours. The surface density of bacteria at the point of positive detection was 15 ± 4 bacteria per mm2. Our approach offers great potential for the development of fast specific detection systems based on affinity monitoring.

Dynamic mathematical modeling of heat and mass transfer incorporating with the local nutrient and biomass limitation of growth in a packed-bed solid-state bioreactor

This research develops on our previous semi-mechanistic model that describes the dynamic physical and biochemical processes taking place in a packed-bed bioreactor to analyze the relationship of nutrient limitation, biomass accumulation, metabolic heat generation, and mathematical description of packed-bed porous media. The experimental and simulation data proved that glucose concentration gradients in the biofilm could be neglected due to small biofilm thickness and high diffusivity of glucose in the biofilm. The prediction results also showed that an increase in the initial substrate concentration leads to a rise in the temperature gradient in the bed. The model proposes that if the diameter of substrate particle is too large (r > 0.1 cm), the growth rate will decrease significantly due to the high biomass accumulation in the biofilm, and temperature gradients decrease in the column. This can be used as a strategy to control the overheating problem in the bed.

Predictive Model for Growth of Bacillus cereus at Temperatures Applicable to Cooling of Cooked Pasta

A model was developed to predict the growth of Bacillus cereus from spores during cooling of cooked pasta. Cooked pasta was inoculated with a cocktail of four strains of heat-shocked (80 °C/10 min) B. cereus spores to obtain a final spore concentration of approximately 2 log CFU/g. Thereafter, growth was determined at isothermal temperatures starting at 10 °C and every three degrees up to 49 °C. Samples were removed periodically and plated on mannitol egg yolk polymyxin agar. The plates were incubated for 24 hr at 30 °C. Baranyi, Huang, and modified Gompertz primary growth models were used to fit growth data. The modified Ratkowsky secondary model was used to fit growth rates determined by the primary growth models with respect to temperature. All three primary models fitted the growth data well. The modified Ratkowsky secondary model adequately fit growth rates generated by the three primary models (R² values ranging from 0.96 to 0.98). After acceptable prediction zone (APZ) validation and goodness of fit statistical analyses, it was determined that the Baranyi primary growth model was best suited for these data. For both single-rate exponential cooling and biphasic linear cooling model validation, all Baranyi model predictions (n = 24 and 28, respectively) fell within the APZ (-1.0 to 0.5 log CFU/g). The model will assist institutional food service settings to determine the safety of cooked pasta subjected to longer cooling times or stored at improper temperatures. PRACTICAL APPLICATION: Predictive model can be used to estimate extent of microbial growth during cooling of cooked pasta and in designing HACCP program and setting of critical control levels. Retail food industry would need fewer challenge studies to validate the safety of their products. The model will provide regulatory agencies and food industry with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning.

The Effect of Salt and Temperature on the Growth of Fresco Culture

The effect of environmental factors, including temperature and water activity, has a considerable impact on the growth dynamics of each microbial species, and it is complicated in the case of mixed cultures. Therefore, the aim of this study was to describe and analyze the growth dynamics of Fresco culture (consisting of 3 different bacterial species) using predictive microbiology tools. The growth parameters from primary fitting were modelled against temperature using two different secondary models. The intensity of Fresco culture growth in milk was significantly affected by incubation temperature described by Gibson’s model, from which the optimal temperature for growth of 38.6 °C in milk was calculated. This cardinal temperature was verified with the Topt = 38.3 °C calculated by the CTMI model (cardinal temperature model with inflection), providing other cardinal temperatures, i.e., minimal Tmin = 4.0 °C and maximal Tmax = 49.6 °C for Fresco culture growth. The specific growth rate of the culture under optimal temperature was 1.56 h−1. The addition of 1% w/v salt stimulated the culture growth dynamics under temperatures down to 33 °C but not the rate of milk acidification. The prediction data were validated and can be used in dairy practice during manufacture of fermented dairy products.

Quantitative Microbial Risk Assessment of Salmonellosis from the Consumption of Australian Pork: Minced Meat from Retail to Burgers Prepared and Consumed at Home

Pork burgers could be expected to have an elevated risk of salmonellosis compared to other pork products due to their comminuted nature. A stochastic risk assessment was performed to estimate the risk of salmonellosis from Australian pork burgers and considered risk-affecting factors in the pork supply chain from retail to consumption at home. Conditions modeled included prevalence and concentration of Salmonella in pork mince, time and temperature effects during retail, consumer transport, and domestic storage and the effect of cooking, with the probability of illness from consumption estimated based on these effects. The model was two-dimensional, allowing for the separation of variability and uncertainty. Potential changes to production practices and consumer behaviors were examined through alternative scenarios. Under current conditions in Australia, the mean risk of salmonellosis from consumption of 100 g pork burgers was estimated to be 1.54 × 10 - 8 per serving or one illness per 65,000,000 servings consumed. Under a scenario in which all pork mince consumed is served as pork burgers, and with conservative (i.e., worst-case) assumptions, 0.746 cases of salmonellosis per year from pork burgers in Australia were predicted. Despite the adoption of several conservative assumptions to fill data gaps, it is predicted that pork burgers have a low probability of causing salmonellosis in Australia.

Metabolic Theory and the Temperature-Size Rule Explain the Temperature Dependence of Population Carrying Capacity

The temperature dependence of highly conserved subcellular metabolic systems affects ecological patterns and processes across scales, from organisms to ecosystems. Population density at carrying capacity plays an important role in evolutionary processes, biodiversity, and ecosystem function, yet how it varies with temperature-dependent metabolism remains unclear. Though the exponential effect of temperature on intrinsic population growth rate, r, is well known, we still lack clear evidence that population density at carrying capacity, K, declines with increasing per capita metabolic rate, as predicted by the metabolic theory of ecology (MTE). We experimentally tested whether temperature effects on photosynthesis propagate directly to population carrying capacity in a model species, the mobile phytoplankton Tetraselmis tetrahele. After maintaining populations at a fixed resource supply and fixed temperatures for 43 days, we found that carrying capacity declined with increasing temperature. This decline was predicted quantitatively when models included temperature-dependent metabolic rates and temperature-associated body-size shifts. Our results demonstrate that warming reduces carrying capacity and that temperature effects on body size and metabolic rate interact to determine how temperature affects population dynamics. These findings bolster efforts to relate metabolic temperature dependence to population and ecosystem patterns via MTE.

Effects of Temperature and Moisture on the Infection and Development of Apple Fruit Rot Caused by Phytophthora cactorum

Phytophthora fruit rot, caused by Phytophthora cactorum, is an important disease of apple in China, often causing more than 50% fruit rot in rainy years. We examined the effects of temperature and moisture on the development of the disease and effects of the variables on zoospore release and germination, infection, and lesion development. In vitro, a temperature range of 5 to 20°C had no significant effects on zoospore release dynamics but did significantly affect the quantities of released zoospores. The largest quantity of zoospores was released at 9.9°C according to a fitted model. Zoosporangia released zoospores within 15 min at the test temperatures (0 to 20°C), which peaked at the fourth hour. Zoospores germinated in vitro, requiring free water, at temperatures from 5 to 35°C. The optimum germination temperature was 25.1°C according to a fitted model. The minimum wetness duration required for zoospores to complete the infection process and induce visible lesions on Fuji fruit was 0.40 h at the optimal temperature of 23.0°C according to the fitted model, whereas observed values were 4.5, 1.5, 0.5, 1.5 and 8.5 h at 10, 15, 20, 25, and 30°C, respectively. The number of zoospore infections on fruit at various temperatures and wetness durations were well fitted by the modified Weibull model; based on the model, the optimal temperature for zoospore infections was 23.0°C. Young apple fruit infected by zoospores developed visible lesions from 10 to 30°C, with a predicted optimum of 23.5°C; no lesions developed at 5 or 35°C. The shortest incubation period of the disease was 4 days. These results can be used to develop disease forecasting models for improved fungicide control.

Integrating Dispersants in Oil Spill Response in Arctic and Other Icy Environments

Future oil exploration and marine navigation may well extend into the Arctic Ocean, and government agencies and responders need to plan for accidental oil spills. We argue that dispersants should play an important role in these plans, since they have substantial logistical benefits, work effectively under Arctic conditions, and stimulate the rapid biodegradation of spilled oil. They also minimize the risk of surface slicks to birds and mammals, the stranding of oil on fragile shorelines and minimize the need for large work crews to be exposed to Arctic conditions.