Model Of Bacterial Growth Research Articles

Single-step hydrothermal synthesis of zinc oxide nanorods for potential use as nano-antibiotics without seeding or bases.

The alarming global rise in antibiotic resistance, driven by the widespread overuse of traditional antibiotics, has created an urgent demand for new antimicrobial solutions. This study presents zinc oxide (ZnO) nanorods as a potential nano-antibiotic agent. ZnO nanorods, with a 2:3 aspect ratio, were synthesized using an efficient one-step hydrothermal method at a low temperature of 100°C, reducing the synthesis time to just 5 hours. The synthesized ZnO nanorods' morphology, structure, and composition were characterized using scanning electron microscopy, x-ray diffraction, and energy dispersive x-ray spectroscopy. The potent antimicrobial activity of these nanorods against common bacterial strains such as Escherichia coli, Bacillus subtilis, and Vibrio parahaemolyticus was examined through optical density at 600 nm (OD600) measurements and inhibition zone analysis, demonstrating substantial inhibition of bacterial growth. In particular, at a concentration of 5 mg/mL, ZnO nanorods achieved a 96% reduction of B. subtilis bacteria in OD600 and an impressive 99.87% reduction in culturing assays within one day, showcasing bactericidal efficiency on par with tetracycline at 0.003 mg/mL. Furthermore, a predictive model of bacterial growth was developed and validated, providing insights into the time-dependent bactericidal efficiency of the synthesized nanorods. These results highlight the potential of ZnO-based composites as a promising solution to combat antibiotic resistance, paving the way for next-generation antimicrobial materials.

Comparison of Glucose Metabolizing Properties of Enterobacterial Probiotic Strains In Vitro.

Before the absorption in the intestine, glucose encounters gut bacteria, which may serve as a barrier against hyperglycemia by metabolizing glucose. In the present study, we compared the capacity of enterobacterial strains to lower glucose levels in an in vitro model of nutrient-induced bacterial growth. Two probiotic strains, Hafnia alvei HA4597 (H. alvei) and Escherichia coli (E. coli) Nissle 1917, as well as E. coli K12, were studied. To mimic bacterial growth in the gut, a planktonic culture was supplemented twice daily by the Luria Bertani milieu with or without 0.5% glucose. Repeated nutrient provision resulted in the incremental growth of bacteria. However, in the presence of glucose, the maximal growth of both strains of E. coli but not of H. alvei was inhibited. When glucose was added to the culture medium, a continuous decrease in its concentration was observed during each feeding phase. At its highest density, H. alvei displayed more efficient glucose consumption accompanied by a more pronounced downregulation of glucose transporters' expression than E. coli K12. Thus, the study reveals that the probiotic strain H. alvei HA4597 is more resilient to maintain its growth than E. coli in the presence of 0.5% glucose accompanied by more efficient glucose consumption. This experimental approach offers a new strategy for the identification of probiotics with increased glucose metabolizing capacities potentially useful for the prevention and co-treatment of type 2 diabetes.

Simple bacterial growth model for the formation of spontaneous and triggered dormant subpopulations

Bacterial persistence is a phenomenon where a subpopulation of cells can survive antibiotic treatment, and it is often linked to extremely slow growth or a dormant state. However, the mechanisms and factors that govern dormancy are not well understood. We propose a simplified growth model that treats the main cellular components as discrete variables and allocates resources among them according to different strategies. The model can reproduce some of the observed features of bacterial persistence, such as wide distribution in division times, long division times after a nutrient downshift, and the existence of different dormant phenotypes. We also show how the growth structure, i.e., whether cells grow in a lineage or a branch, affects the dormant cells' occurrence and distribution due to the growth states' mother-daughter correlation. Our model provides a framework to explore the complex interactions between cellular processes and environmental conditions that lead to bacterial persistence. Published by the American Physical Society 2024

Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models

Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation’s output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model’s output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

Logistic Model for Predicting Coliform Growth in Wastewater Effluent From the Chambo River (Ecuador)

Purpose: The objective of this work is to apply a predictive model of bacterial growth to determine the concentration of total coliforms, under controlled laboratory conditions, in wastewater from the city of Riobamba that flows into the Chambo River (Ecuador). Theoretical Framework: Wastewater pollution is a global problem that affects water quality and public health. Population growth has a drastic impact on the contamination of water bodies, particularly in Ecuador, since most of the effluents are not adequately treated, which becomes a public health and environmental problem. For this reason, over the years, total and fecal coliforms have been used as indicators of water quality. Design/Methodology/Approach: The study used a mathematical modeling approach of a logistic and deterministic type, and calculated through an executable command designed in the integrated development environment (IDE) with the Visual C# programming language (logic) and the XAML design language (interface). Findings: The results achieved are 1) the theoretical construction of a mathematical model (logistic model) starting from a differential equation and the taking of three experimental measurements, 2) the computer construction of the bacterial growth model and 3) it has been demonstrated that there is agreement between the values obtained from the model of total coliform growth and the experimental results with a margin of error of less than 1%. Research, Practical & Social Implications: This information can be used by stakeholders to determine actions that could improve current conditions. Originality/Value: The result of this study emphasizes that the model proposed can reproduce the growth of coliforms up to a maximum growth point, it is necessary to continue with the development of the same in the stationary phase of bacterial growth and then in the decay phase.

Logistic Model for Predicting Coliform Growth in Wastewater Effluent From the Chambo River (Ecuador)

Purpose: The objective of this work is to apply a predictive model of bacterial growth to determine the concentration of total coliforms, under controlled laboratory conditions, in wastewater from the city of Riobamba that flows into the Chambo River (Ecuador). Theoretical Framework: Wastewater pollution is a global problem that affects water quality and public health. Population growth has a drastic impact on the contamination of water bodies, particularly in Ecuador, since most of the effluents are not adequately treated, which becomes a public health and environmental problem. For this reason, over the years, total and fecal coliforms have been used as indicators of water quality. Design/Methodology/Approach: The study used a mathematical modeling approach of a logistic and deterministic type, and calculated through an executable command designed in the integrated development environment (IDE) with the Visual C# programming language (logic) and the XAML design language (interface). Findings: The results achieved are 1) the theoretical construction of a mathematical model (logistic model) starting from a differential equation and the taking of three experimental measurements, 2) the computer construction of the bacterial growth model and 3) it has been demonstrated that there is agreement between the values obtained from the model of total coliform growth and the experimental results with a margin of error of less than 1%. Research, Practical & Social Implications: This information can be used by stakeholders to determine actions that could improve current conditions. Originality/Value: The result of this study emphasizes that the model proposed can reproduce the growth of coliforms up to a maximum growth point, it is necessary to continue with the development of the same in the stationary phase of bacterial growth and then in the decay phase.

Stability analysis of a bacterial growth model through computer algebra

Stability analysis of a bacterial growth model through computer algebra

Obtaining lactic acid through microbial fermentation from corn residues

The production and kinetics of lactic acid (LA) by batch fermentation were studied, using corn residues as substrate with Lactobacillus delbrueckii spp. Bulgaricus. A factorial design was developed for the pretreatment stage of the residue and lactic fermentation to determine the best conditions for obtaining total reducing sugars and lactic acid. In the pretreatment, the best conditions were obtained by working with HCl at 3% (v/v), 75 °C, and 30 minutes of reaction. Fermentation was carried out, keeping the medium at a pH between 5.5-6, and the best working conditions were 100 µl of the microorganism, 37ºC, and 24 hours of fermentation, reaching a maximum concentration of 19.9 g/L. Kinetic models for LA production, bacterial growth, and substrate consumption were proposed. Likewise, to verify the observed data, different statistical indices were calculated to evaluate the accuracy of the models. Finally the Luedeking-Piret and logistic models, were selected as they presented the best fit to the experimental data for LA production, substrate consumption, and bacterial growth, respectively.

Stochastic Modeling of Bacterial Population Growth with Antimicrobial Resistance

In this paper we consider a stochastic model of bacterial population growth with antimicrobial resistance under the influence of random fluctuations. We analyze the model for the problem of persistence and extinction of bacterial cells. This analysis shows asymptotic extinction and conditional persistence for growing population. Moreover, we perform computer simulations in order to illustrate the model behavior. The model results have important implications for the eradication of bacterial cells and the emergence of resistance.

Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth

Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of Escherichia coli on a glucose–lactose mixture we show results accurately reproduce experiments. We show that for a single strain of E. coli, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.

Generalized logistic model of bacterial growth

ABSTRACT This work proposes a new mathematical model describing the dynamics of growing bacterial cultures. The model, described by a first order non-linear differential equation, as a generalization of the logistic equation, was compared with the most studied mathematical models. All models were numerically implemented and fitted to the experimental data, collected from the incubation of a bacterial strain of Pseudomonas fluorescens, to obtain the growth parameters. The experimental data showed the lowest fit error for both the Baranyi–Roberts and new models, which turned out to be equivalent. Simulations of the fitting algorithm were also implemented and repeated for a large number of initial guesses of the parameters, chosen in order to test the fitting and convergence performances. The innovative feature that makes the new model easier to use than Baranyi–Roberts model is definitely its simple and manageable analytical form and its good performance in terms of convergence time.

Predicting Single Cell Lag Time and Maximum Specific Growth Rate of Proteus mirabilis using Curve Fitting Machine Learning Algorithm (MLA)

The lack of adequate assessment methods for pathogens especially in food is a critical problem in microbiology. Traditional predictive methods are not able to accurately describe the trend of low-density bacterial growth behavior observed in the laboratory. The purpose of this study was to leverage state-of-the-art of machine learning algorithms (MLA) to develop a predictive model for bacterial growth of Proteus mirabilis after treatment of bay leaf extract. The experimental data are fitted to three models, namely logistic, Gompertz, and Richard models. These models are trained using simulation data and a curve-fitting optimization algorithm in MATLAB called fminsearch is applied to the data to obtain the optimal parameters of the models. The results show that this method provides a breakthrough in bacterial growth modeling. Various forms of mathematical models such as Gompertz, Richard, and others are no longer necessary to model bacterial behavior. Additionally, the generated model can help microbiologists in understanding the growth characteristics of bacteria after disinfectant treatment, and provides a theoretical reference and a method of risk management for better assessment of pathogens in food.

Well posedness and stochastic derivation of a diffusion-growth-fragmentation equation in a chemostat

We study the existence and uniqueness of the solution of a non-linear coupled system constituted of a degenerate diffusion-growth-fragmentation equation and a differential equation, resulting from the modeling of bacterial growth in a chemostat. This system is derived, in a large population approximation, from a stochastic individual-based model where each individual is characterized by a non-negative real valued trait described by a diffusion. Two uniqueness results are highlighted. They differ in their hypotheses related to the influence of the resource on individual trait dynamics, the main difficulty being the non-linearity due to this dependence and the degeneracy of the diffusion coefficient. Further we show that the semi-group of the stochastic trait dynamics admits a density by probabilistic arguments, that allows the measure solution of the diffusiongrowth-fragmentation equation to be a function with a certain Besov regularity.

Electricity-reaction–diffusion system for microbially induced carbonate precipitation

Microbially induced carbonate precipitation (MICP) has attracted attention as a novel soil-improvement technique. The precipitation and growth of calcium carbonate on the surface of sand and in its pores can be simulated using the recently proposed numerical simulation techniques. However, these simulations have not incorporated certain parameters, such as electrical properties and adsorption. The current study proposes a novel coupling system that considers the adsorption of microorganisms and dissolved ions due to the weak electrostatic field generated on the surface of sand. Additionally, the Michaelis–Menten model was introduced into the system for the enzymatic reaction of ureolytic bacteria, the Monod model for bacterial growth, and a chemotaxis model. To validate the proposed simulation model, previously reported experimental and computational results were compared with the suggested simulation results. Consequently, the calcium carbonate precipitation obtained with the simulation model was 0.335 μmol/mm3 at 10.3 h, which is close to that obtained by the experiment of 0.315 μmol/mm3. Moreover, the spatial distribution of calcium carbonate was consistent with the precipitation pattern of the active bonding structure obtained by the traditional experiment.

Computer-assisted modeling of Quorum sensing in bacterial population exposed to antibiotics

A mathematical model for bacterial growth and control by antibiotics treatment, including Quorum sensing as a special kind of communication, is introduced. We aim in setting up a flexible model structure allowing for fast simulations and overview about the general behavior. The deterministic approach can be used for in silico studies of bacterial cooperative behavior in the special case of Quorum sensing. Since antibiotic treatment is the basic and vital way to fight pathogenic bacteria, in the present study, we propose a modification of a reaction-diffusion model of communication processes in a bacterial population exposed to antibiotics. The dynamical biological system is formalized by a system of semilinear parabolic PDEs. The numerical solution of the 2D problem is based on a hybrid computing procedure, which includes a finite difference method combined with a Monte-Carlo simulation of population dynamics. Computational experiments are performed to describe space-time distributions of key chemical compounds characterizing Quorum sensing during the growth of a bacterial population and its decrease resulting from the predetermined strategy of antibiotic treatment.

A compressed logistic equation on bacteria growth: inferring time-dependent growth rate

We propose a compressed logistic model for bacterial growth by invoking a time-dependent rate instead of the intrinsic growth rate (constant), which was adopted in traditional logistic models. The new model may have a better physiological basis than the traditional ones, and it replicates experimental observations, such as the case example for E. coli, Salmonella, and Staphylococcus aureus. Stochastic colonial growth at a different rate may have a fractal-like nature, which should be an origin of the time-dependent reaction rate. The present model, from a stochastic viewpoint, is approximated as a Gaussian time evolution of bacteria (error function).

Uso de vinazas de mezcal para producir metano por co-digestión con estiércol de bovino

The best co-digestion ratio between mezcal vinasses and bovine manure was investigated to produce methane.Five vinasses and bovine manure mixtures in different proportionswere compared (v/v): 25-75, 50-50, 75-25, 100-0, and 0-100, with six replications. As reactors, 496 mL glasscontainers with hermetic seal were used, with 300 mL of active volume under mesophilic conditions (36 ± 1 oC) for 10 days. The pH, total solids, and volatile solids of vinasses,bovine manure, and their mixtures were determined in triplicate.The methane accumulated volume was greater with the 0-100 and 75-25, followed by the 100-0, with 286.23, 286.40, and 225.48 NmL CH4, respectively. Methane yield was higher with 100-0 and 75-25 with 28.27 and 22.77 NmL CH4 g-1volatile solids, respectively. The Gompertz bacterial growthmodel showed that the micro-organisms adaptation periodin vinasses was longer than in bovine manure. Co-digestionof vinasses and bovine manure in proportion 75-25 % improvedthe methane production by 26.7 % with respect to the digestion of vinasses alone, and reduced the adaptation timeof bacteria to vinasses by 4.12 days.

Effects of chemical pretreatments on material solubilization of Areca catechu L. husk: Digestion, biodegradability, and kinetic studies for biogas yield

This study aimed to understand the pretreatment-aided anaerobic digestion of lignocellulosic residues and to assess the substrate solubilization capacity of pretreatment processes. We evaluated the feasibility of biogas production using chemically pretreated Areca catechu L. (Arecanut husk, AH). AH was pretreated for 24h at two different temperatures—25 °C and 90 °C with four different chemicals viz. H2SO4 (acidic), NaOH (alkaline), H2O2 (oxidative), and ethanol in 1% H2SO4 (organosolv) under each temperature. AH solubilization assessment included analyses of parameters such as volatile solids to total solids (VS:TS) ratio, soluble chemical oxygen demand, total phenolic content, and biomass composition. Alkaline pretreatment of AH at 90 °C resulted in the maximum biogas yield of 683.89mL/gVS, which was 2.3 times more than that obtained using raw AH without pretreatment. Methane content of biogas produced using AH pretreated with 2–10% of NaOH was found to be between 71.53% and 75.06%; methane content of biogas using raw AH was 62.31%. In order to describe the AH degradation patterns, biogas production potential from pretreated AH was evaluated using bacterial kinetic growth models (First-order exponential, logistic, transference, and modified Gompertz models). The modified Gompertz and logistic models (correlation coefficient >0.99) were found to have the best fit of all kinetic models for the cumulative experimental biogas curve. We formulated a multiple linear regression equation depicting the biodegradability index (BI) as a technical tool to determine biomethane production; BI is represented as a function of biomass composition (cellulose, hemicellulose, and lignin), with a high correlation (>0.95). Based on our analyses of AH pretreatment and substrate utilization for biogas production, we propose that the biochemical composition of lignocellulosic residues should be carefully considered to ensure their biodegradability when subjected to anaerobic digestion.

Applications of Coarse-Grained Models in Metabolic Engineering.

Mathematical modeling is a promising tool for better understanding of cellular processes. In recent years, the development of coarse-grained models has gained attraction since these simple models are able to capture and describe a broad range of growth conditions. Coarse-grained models often comprise only two cellular components, a low molecular component as representative for central metabolism and energy generation and a macromolecular component, representing the entire proteome. A framework is presented that presents a strict mass conservative model for bacterial growth during a biotechnological production process. After providing interesting properties for the steady-state solution, applications are presented 1) for a production process of an amino acid and 2) production of a metabolite from central metabolism.

Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance.

Antimicrobial resistance is a major public health issue. The pharmacokinetic/pharmacodynamic (PK/PD) model is an essential tool to optimize dosage regimens and alleviate the emergence of resistance. The semi-mechanistic PK/PD model is a mathematical quantitative tool to capture the relationship between dose, exposure, and response, in terms of the mechanism. Understanding the different resistant mechanisms of bacteria to various antibacterials and presenting this as mathematical equations, the semi-mechanistic PK/PD model can capture and simulate the progress of bacterial growth and the variation in susceptibility. In this review, we outline the bacterial growth model and antibacterial effect model, including different resistant mechanisms, such as persisting resistance, adaptive resistance, and pre-existing resistance, of antibacterials against bacteria. The application of the semi-mechanistic PK/PD model, such as the determination of PK/PD breakpoints, combination therapy, and dosage optimization, are also summarized. Additionally, it is important to integrate the PD effect, such as the inoculum effect and host response, in order to develop a comprehensive mechanism model. In conclusion, with the semi-mechanistic PK/PD model, the dosage regimen can be reasonably determined, which can suppress bacterial growth and resistance development.