More than simply microbial growth curves

Bacterial growth is a very important piece of information in a wide range of investigation and, in most of the time the data are simply shown directly without any further processing. In a single factor investigation without comparative information to be extracted, this simple approach can be used together with other data to form a comprehensive set of results. When comparison is involved, such direct showing of bacterial growth curves without processing cannot warrant a meaningful comparison thoroughly and further processing of data is necessary. In addition, there is little, if any, quantitative data for the comparison from the display of growth curves and description of a number of curves is not a simple task, especially in a meaningful way for assimilation of the data to readers. With this in mind, I would like to remind of those who plan to show such data as growth curves for their potential publication to carry this further to generate comparative results for a much meaningful interpretation by modeling and calculation from the raw growth data over time of incubation. By calculating with existing equations, the lag phase, growth rate and the biomass can be derived from a series of growth curves for a more effective and meaningful analysis. This approach is not new, but remembrance of such available tool is more important so that research data are shown professionally and also scientifically for meaning presentation and effective assimilation.

Download Full-text

On the lag phase and initial decline of microbial growth curves

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2006.08.017 ◽

2007 ◽

Vol 244 (3) ◽

pp. 511-517 ◽

Cited By ~ 35

Author(s):

George T. Yates ◽

Thomas Smotzer

Keyword(s):

Microbial Growth ◽

Growth Curves ◽

Lag Phase

Download Full-text

Bacteria Exposed to Silver Nanoparticles Synthesized by Laser Ablation in Water: Modelling E. coli Growth and Inactivation

Materials ◽

10.3390/ma13030653 ◽

2020 ◽

Vol 13 (3) ◽

pp. 653 ◽

Cited By ~ 2

Author(s):

Lucija Krce ◽

Matilda Šprung ◽

Ana Maravić ◽

Polona Umek ◽

Krešimir Salamon ◽

...

Keyword(s):

Silver Nanoparticles ◽

Bacterial Growth ◽

Growth Curves ◽

Dynamic State ◽

Lag Phase ◽

The Novel ◽

Minimal Inhibitory Concentrations ◽

E Coli ◽

Main Effect

This study is aimed to better understand the bactericidal mode of action of silver nanoparticles. Here we present the production and characterization of laser-synthesized silver nanoparticles along with growth curves of bacteria treated at sub-minimal and minimal inhibitory concentrations, obtained by optical density measurements. The main effect of the treatment is the increase of the bacterial apparent lag time, which is very well described by the novel growth model as well as the entire growth curves for different concentrations. The main assumption of the model is that the treated bacteria uptake the nanoparticles and inactivate, which results in the decrease of both the nanoparticles and the bacteria concentrations. The lag assumes infinitive value for the minimal inhibitory concentration treatment. This apparent lag phase is not postponed bacterial growth. It is a dynamic state in which the bacterial growth and death rates are close in value. Our results strongly suggest that the predominant mode of antibacterial action of silver nanoparticles is the penetration inside the membrane.

Download Full-text

Use of the Microbial Growth Curve in Postantibiotic Effect Studies of Legionella pneumophila

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.47.3.1081-1087.2003 ◽

2003 ◽

Vol 47 (3) ◽

pp. 1081-1087 ◽

Cited By ~ 10

Author(s):

Raymond P. Smith ◽

Aldona L. Baltch ◽

Phyllis B. Michelsen ◽

William J. Ritz ◽

Richard Alteri

Keyword(s):

Minimum Inhibitory Concentration ◽

Bacterial Growth ◽

Legionella Pneumophila ◽

Microbial Growth ◽

Inhibitory Concentration ◽

Growth Curves ◽

Laboratory Medicine ◽

Postantibiotic Effect ◽

Antibiotic Concentration

ABSTRACT Using the standard Craig and Gudmundsson method (W. A. Craig and S. Gudmundsson, p. 296-329, in V. Lorian, ed., Antibiotics in Laboratory Medicine, 1996) as a guideline for determination of postantibiotic effects (PAE), we studied a large series of growth curves for two strains of Legionella pneumophila. We found that the intensity of the PAE was best determined by using a statistically fitted line over hours 3 to 9 following antibiotic removal. We further determined the PAE duration by using a series of observations of the assay interval from hours 3 to 24. We determined that inoculum reduction was not necessarily the only predictor of the PAE but that the PAE was subject to the type and dose of the drug used in the study. In addition, there was a variation between strains. Only levofloxacin at five and ten times the minimum inhibitory concentration (MIC) resulted in a PAE duration of 4 to 10 h for both strains of L. pneumophila tested. Ciprofloxacin at five and ten times the MIC and azithromycin at ten times the MIC caused a PAE for one strain only. No PAE could be demonstrated for either strain with erythromycin or doxycycline. Using the presently described method of measuring PAE for L. pneumophila, we were able to detect differences in PAE which were dependent upon the L. pneumophila strain, the antibiotic tested, and the antibiotic concentration. We suggest the use of mathematically fitted curves for comparison of bacterial growth in order to measure PAE for L. pneumophila.

Download Full-text

Validation of Predictive Mathematical Models Describing the Growth of Listeria monocytogenes

Journal of Food Protection ◽

10.4315/0362-028x-60.9.1142 ◽

1997 ◽

Vol 60 (9) ◽

pp. 1142-1145 ◽

Cited By ~ 14

Author(s):

ISABEL WALLS ◽

VIRGINIA N. SCOTT

Keyword(s):

Listeria Monocytogenes ◽

Growth Rates ◽

Microbial Growth ◽

Lag Phase ◽

Growth Data ◽

Ph Values ◽

Gompertz Equation ◽

Generation Times ◽

Different Temperatures ◽

Good Agreement

Growth of Listeria monocytogenes and Listeria innocua in commercially available sterile homogeneous foods was investigated at different temperatures, pH values, and NaCl concentrations. Growth data were fitted to the Gompertz equation and the resulting growth kinetics were compared with predictions from the Pathogen Modeling Program and Food MicroModel. In general, good agreement was obtained when comparing growth rates and generation times for both models. Differences were observed when comparing lag phases, which ranged from 117 h shorter to 4.9 h longer than predicted for L. monocytogenes. Despite differences in lag phase, under most conditions, the models gave good predictions of microbial growth. Predictive modeling appears to be a useful tool in determining growth rates of Listeria in foods.

Download Full-text

The Process Pathway Model of bacterial growth

10.1101/553982 ◽

2019 ◽

Author(s):

Daniel Biro ◽

Ximo Pechuan ◽

Maryl Lambros ◽

Aviv Bergman

Keyword(s):

Temperature Dependence ◽

Bacterial Growth ◽

Large Scale ◽

Growth Curves ◽

Population Level ◽

Growth Data ◽

Pathway Model ◽

Growth Profile ◽

Simplifying Assumptions ◽

Multiple Species

AbstractThe growth profile of microorganisms in an enclosed environment, such as a bioreactor or flask, is a well studied and characterized system. Despite a long history of examination, there are still many competing mathematical models used to describe an output of the microorganisms, namely the number of bacteria as a function of time. However, these descriptions are either purely phenomenological and give no intuition as to the biological mechanisms underlying the growth curves, or extremely complex and become computationally unfeasible at the population level. In this paper, we develop the Process Pathway Model by modifying a model of sequential processes, which was first used to model robustness in metabolic pathways, and demonstrate that the Process Pathway Model encapsulates many features and temperature dependence of bacterial growth. We verify the predictions of the model against growth data for multiple species of microorganisms, and confirm that the model generates accurate predictions on temperature dependence of bacterial growth. The model has five free parameters, and the simplifying assumptions used to build the model are built upon biologically realistic notions. The Process Pathway Model accurately models a microorganism’s growth profile at an intermediate level of complexity that is computationally feasible. This model can be used as both an conceptual model for thinking about systems of bacterial growth, as well as a computational model that operates at level of complexity that is amenable to large scale simulation. This balance in accuracy and intuitiveness was accomplished by using realistic biological assumptions to simplify the underlying biology, which may point the way forward for future models of this type.

Download Full-text

The Effect of Gibberellic Acid on the Production Characteristics and Biochemical Parameters of Tetraselmis Suecica in an Enrichment Culture

KnE Life Sciences ◽

10.18502/kls.v7i1.10125 ◽

2022 ◽

Author(s):

Nikolai Nikolaevich Kovalev ◽

Svetlana Yevgenyevna Leskova ◽

Yevgeny Valeryevich Mikheev ◽

Yulia Mikhailovna Pozdnyakova ◽

Roman Vladimirovich Esipenko

Keyword(s):

Gibberellic Acid ◽

Enrichment Culture ◽

Growth Curves ◽

Growth Dynamics ◽

Control Group ◽

Lag Phase ◽

Low Concentrations ◽

Wide Range ◽

Microalgae Biomass ◽

Microalgae Growth

The use of gibberellic acid as a stimulator of microalgae growth has beensubstantiatedexperimentally.This research aimed to assess the effect of exposure to a wide range of gibberellic acid concentrations on the growth dynamics ofthe microalgaTetraselmissuecicain an enrichment culture. The duration of the experiments was 14 days. It has been shown that gibberellic acid,atconcentrations of 0.39–3.20× 10−8M, stimulates algaegrowth. In this research, the exposure to gibberellic acid at concentrations of 0.39–3.20 × 10−8M was accompanied by a variation in the pattern of growth curves: the maximum number of cells was recorded on day seven of the experiment. A higher concentration of the phytohormone (3.84 × 10−8М) inhibited the increase inculture density. The growth of theT. suecicaculture in the control group was 332%;the growth of the culture exposed to gibberellic acid at a concentration of 0.39 × 10−8M was1136%. The values of the specific growth rate ofT. suecicawere estimated for different periods of cultivation. On day14 of the experiment, the biochemical composition of microalgae biomass was analyzed.According to the results, gibberellic acid stimulated the accumulation of carbohydrates, proteins, and chlorophyll. Nevertheless, the phytohormone had no effect on lipidaccumulation. An assumption was made thatexposure to low concentrations of phytohormone stimulates the growth of microalgae by reducing the lag phase of growth. Keywords: gibberellic acid, microalga, cultivation, lipids, carbohydrates, proteins

Download Full-text

Detecting differential growth of microbial populations with Gaussian process regression

10.1101/055186 ◽

2016 ◽

Author(s):

Peter D Tonner ◽

Cynthia L Darnell ◽

Barbara E Engelhardt ◽

Amy K Schmid

Keyword(s):

Population Growth ◽

Gaussian Process ◽

Microbial Growth ◽

Microbial Population ◽

Statistical Tests ◽

Growth Curves ◽

Biological Knowledge ◽

Growth Data ◽

Differential Growth ◽

Differential Effects

AbstractMicrobial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions that produce a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit non-standard growth curves. Rather than fix expected functional forms of different experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high resolution time-series growth data enables accurate quantification of population growth, and can be extended to identify differential growth phenotypes due to genetic background or stress. Additionally, confounding effects due to experimental variation can be controlled explicitly. Our framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model to a collection of growth measurements for seven transcription factor knockout strains of a model archaeal organism,Halobacterium salinarum. Using these models fitted to growth data, two statistical tests were developed to quantify the differential effects of genetic and environmental perturbations on microbial growth. These statistical tests accurately identify known regulators and implicate novel regulators of growth under standard and stress conditions. Furthermore, the fitted GP regression models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

Download Full-text

Effect of erythritol and xylitol on Streptococcus pyogenes causing peritonsillar abscesses

Scientific Reports ◽

10.1038/s41598-021-95367-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Siiri Kõljalg ◽

Risto Vaikjärv ◽

Imbi Smidt ◽

Tiiu Rööp ◽

Anirikh Chakrabarti ◽

...

Keyword(s):

Streptococcus Pyogenes ◽

Bacterial Growth ◽

Growth Curves ◽

Culture Collection ◽

Exponential Phase ◽

Clinical Isolates ◽

Growth Inhibitors ◽

Throat Culture ◽

Potent Growth

AbstractPolyols are effective against caries-causing streptococci but the effect on oropharynx-derived pyogenic streptococci is not well characterised. We aimed to study the effect of erythritol (ERY) and xylitol (XYL) against Streptococcus pyogenes isolated from peritonsillar abscesses (PTA). We used 31 clinical isolates and 5 throat culture collection strains. Inhibition of bacterial growth by polyols at 2.5%, 5% and 10% concentrations was studied and the results were scored. Amylase levels in PTA pus were compared to polyol effectivity scores (PES). Growth curves of four S. pyogenes isolates were analysed. Our study showed that XYL was more effective than ERY inhibiting 71–97% and 48–84% of isolates, respectively, depending of concentrations. 48% of clinical and all throat strains were inhibited by polyols in all concentrations (PES 3). PES was negative or zero in 26% of the isolates in the presence of ERY and in 19% of XYL. ERY enhanced the growth of S. pyogenes isolated from pus with high amylase levels. Polyols in all concentrations inhibited the growth in exponential phase. In conclusion, ERY and XYL are potent growth inhibitors of S. pyogenes isolated from PTA. Therefore, ERY and XYL may have potential in preventing PTA in the patients with frequent tonsillitis episodes.

Download Full-text

In Vitro Incorporation of Helicobacter pylori into Candida albicans Caused by Acidic pH Stress

Pathogens ◽

10.3390/pathogens9060489 ◽

2020 ◽

Vol 9 (6) ◽

pp. 489 ◽

Cited By ~ 1

Author(s):

Kimberly Sánchez-Alonzo ◽

Cristian Parra-Sepúlveda ◽

Samuel Vega ◽

Humberto Bernasconi ◽

Víctor L. Campos ◽

...

Keyword(s):

Helicobacter Pylori ◽

Candida Albicans ◽

Pcr Amplification ◽

Growth Curves ◽

Acidic Ph ◽

Ph Values ◽

16S Gene ◽

Wide Range ◽

H Pylori

Yeasts can adapt to a wide range of pH fluctuations (2 to 10), while Helicobacter pylori, a facultative intracellular bacterium, can adapt to a range from pH 6 to 8. This work analyzed if H. pylori J99 can protect itself from acidic pH by entering into Candida albicans ATCC 90028. Growth curves were determined for H. pylori and C. albicans at pH 3, 4, and 7. Both microorganisms were co-incubated at the same pH values, and the presence of intra-yeast bacteria was evaluated. Intra-yeast bacteria-like bodies were detected using wet mounting, and intra-yeast binding of anti-H. pylori antibodies was detected using immunofluorescence. The presence of the H. pylori rDNA 16S gene in total DNA from yeasts was demonstrated after PCR amplification. H. pylori showed larger death percentages at pH 3 and 4 than at pH 7. On the contrary, the viability of the yeast was not affected by any of the pHs evaluated. H. pylori entered into C. albicans at all the pH values assayed but to a greater extent at unfavorable pH values (pH 3 or 4, p = 0.014 and p = 0.001, respectively). In conclusion, it is possible to suggest that H. pylori can shelter itself within C. albicans under unfavorable pH conditions.

Download Full-text

Microbial Growth Response to Hydrogel Encapsulated Quantum Dot Nanospheres

MRS Proceedings ◽

10.1557/proc-1064-pp06-17 ◽

2007 ◽

Vol 1064 ◽

Author(s):

Somesree GhoshMitra ◽

Tong Cai ◽

Santaneel Ghosh ◽

Arup Neogi ◽

Zhibing Hu ◽

...

Keyword(s):

Electron Microscopy ◽

Growth Response ◽

Microbial Growth ◽

Biological Effects ◽

Growth Curves ◽

Swelling Properties ◽

Growth Environment ◽

E Coli ◽

Cdte Qds ◽

Biological Compatibility

ABSTRACTQuantum dots (QDs) are now used extensively for labeling in biomedical research due to their unique photoluminescence behavior, involving size-tunable emission color, a narrow and symmetric emission profile and a broad excitation range [1]. Uncoated QDs made of CdTe core are toxic to cells because of release of Cd2+ ions into the cellular environment. This problem can be partially solved by encapsulating QDs with polymers, like poly(N-isopropylacrylamide) (PNIPAM) or poly(ethylene glycol) (PEG). Based on biological compatibility, fast response as well as pH, temperature and magnetic field dependent swelling properties, hydrogel nanospheres has become carriers of drugs, fluorescence labels, magnetic particles for hyperthermia applications and particles that have strong optical absorption profiles for optical excitation. The toxicity of uncoated QDs are known; however, there have been a very limited number of studies specially designed to assess thoroughly the toxicity of nanosphere encapsulated QDs against QD density and dosing level.In this work, we present preliminary studies of biological effects of a novel QD based nanomaterial system on Escherichia coli (E. coli) bacteria. Cadmium chalcogenide QDs provide the most attractive fluorescence labels in comparison with routine dyes or metal complexes. Nanospheres on the other hand are the most commonly used carriers of fluorescence labels for fluorescence detection. The integration of fluorescent QDs in nanospheres therefore provides a new generation of fluorescence markers for biological assays. Hydrogels based on PNIPAM is a well known thermoresponsive polymer that undergoes a volume phase transition across the low critical solution (LCST) [2]. Therefore, the inherent temperature-sensitive swelling properties of PNIPAM offer the potentiality to control QD density within the nanospheres. In the present work, E. coli growth was monitored as E. coli served as a representation of how cells might respond in the presence of hydrogel encapsulated QDs in their growth environment. The present work describes the successful encapsulation of CdTe QDs in PNIPAM gel network. Microgel encapsulated QDs were synthesized by first preparing PNIPAM microspheres with cystaminebisacrylamide as a crosslinker and CdTe QDs capped with a stabilizer. The CdTe QDs were bonded into PNIPAM microgels through the replacement of CdTe's stabilizer inside PNIPAM microspheres. Growth curves were generated for E. coli growing in 20 mL of LB media containing hydrogel encapsulated QD nanospheres (400 nm diameter) at relatively higher (0.5mg/mL) and lower (0.01mg/mL) concentration of solution. From the growth curves, there was no evidence at lower concentration (0.01mg/mL) that the hydrogel encapsulated QDs prevent the microbial cells from growing but at higher concentration (0.5mg/mL), microbial growth was inhibited. Transmission Electron Microscopy (TEM) was used to characterize QD size and density inside the hydrogel nanospheres. Scanning Electron Microscopy (SEM) was used to observe size and morphology of the hydrogel particles. Further investigation is going on cell growth response at different QD density and to evaluate the limiting hydrogel concentration for different QD densities.

Download Full-text