Estimation of bacterial growth rates from turbidimetric and viable count data

The following two factors significantly influence estimates of the maximum specific growth rate ( max ) and the lag-phase duration (): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of max and when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of .The following two methods are commonly used to monitor growth of bacteria: viable count enumeration (VCE) and absorbance measurement. Monitoring bacterial growth by VCE is time-consuming and rather expensive, but it remains the method of reference. Methods based on absorbance measurements constitute a second family of methods based on the direct proportionality between the optical density (OD) of a liquid medium and the concentration of bacteria. OD techniques are rapid, convenient, and inexpensive. However, many drawbacks are inherent to these techniques. The main problem encountered is the limited range of validity since the detection threshold typically corresponds to a bacterial concentration greater than 10 6 bacteria/ml (8). From these two kinds of data, characteristic growth parameters, mainly the lag-phase duration () and the maximum specific growth rate ( max ), can be assessed. The use of mathematical growth models allows accurate and objective estimation of these parameters. In the field of predictive microbiology, numerous models have been developed. Mechanistic models are especially interesting as they provide both a method of estimating and max and a means of understanding bacterial growth.In fact, the following two potential sources of bias influence estimation of growth parameters: the type of data (OD or VCE) and the model used to fit data. Because of the high detection threshold of OD techniques, the initial inoculum must be large enough to allow reliable measurements, and the question which has arisen is whether the estimates of max at high concentrations are not systematically lower than the actual max because of possible end-of-growth inhibition. Nevertheless, this phenomenon seems to have no effect on the estimates of max (9). Hudson and Mott (14) fitted the modified Gompert...

mentioning

confidence: 99%

Modeling the Lag Time of Listeria monocytogenes from Viable Count Enumeration and Optical Density Data

Baty

Flandrois

Delignette-Muller

2002

“…Deterministic mathematical models (e.g., Gompertz and logistic equations) are often proposed as the true underlying "perfect" mechanism and fitted to the data via nonlinear fitting techniques (8,13,16,19,52,59). Deviations from the deterministic predictions are accounted for as observational uncer- tainty.…”

Section: Discussionmentioning

confidence: 99%

Use of Stochastic Models To Assess the Effect of Environmental Factors on Microbial Growth

Ponciano¹,

Vandecasteele

Hess³

et al. 2005

We present a novel application of a stochastic ecological model to the study and analysis of microbial growth dynamics as influenced by environmental conditions in an extensive experimental data set. The model proved to be useful in bridging the gap between theoretical ideas in ecology and an applied problem in microbiology. The data consisted of recorded growth curves of Escherichia coli grown in triplicate in a base medium with all 32 possible combinations of five supplements: glucose, NH 4 Cl, HCl, EDTA, and NaCl. The potential complexity of 2 5 experimental treatments and their effects was reduced to 2 2 as just the metal chelator EDTA, the presumed osmotic pressure imposed by NaCl, and the interaction between these two factors were enough to explain the variability seen in the data. The statistical analysis showed that the positive and negative effects of the five chemical supplements and their combinations were directly translated into an increase or decrease in time required to attain stationary phase and the population size at which the stationary phase started. The stochastic ecological model proved to be useful, as it effectively explained and summarized the uncertainty seen in the recorded growth curves. Our findings have broad implications for both basic and applied research and illustrate how stochastic mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding basic processes in microbial ecology.Mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding the interaction of organisms with their physical and biological environment (8,47,48,50,52,56). Studies in the field of predictive microbiology have shown that successful modeling requires both adequate models and thorough data sets (57; for extensive reviews, see references 10, 42, and 55). In predictive microbiology a two-step modeling approach is used (reference 55 and citations therein). Primary models describe the basic rules of how microbial numbers change over time (52). Next, these simple models are used to derive secondary models that account for the effect of a set of factors in microbial growth.The forces of the environment and the events of reproduction and growth are themselves stochastic in nature (3,21,24,32,36,39,44,45,51,53), yet simple ecological models, such as the Verhulst logistic equation, result in deterministic predictions. However, smooth convergence to asymptotic results is not what is usually seen, even in rigorous experimental settings (17, 31). Hence, a more realistic alternative to population growth modeling is to confront stochastic equations with the data at hand (references 5, 14, and 17 and citations therein and references 41 and 47).In this paper, we describe a novel application of a stochastic population model to analyze how environmental conditions influence microbial growth dynamics using an extensive experimental data set. The primary model used was the stochastic Ricker (SR) equation (27, 51). Our secondary model...

“…A. tumefaciens NT1 (pZLR4) was grown in ABTG medium (9), and C. violaceum CV026 was grown in LB 5 medium (3). Strains of P. phosphoreum isolated in this study (18 strains) as well as 159 strains isolated from previous storage trials with cod and salmon were grown in GMB containing TMAO (18) or on modified Long and Hammer agar (L&H) (13,52) at 15°C. Shewanella strains from this study (67 strains), 36 strains previously isolated from spoiling fish, and 15 strains from culture collections were grown on iron agar (IA; Oxoid CM867) (25) or in brain heart infusion broth (Oxoid CM225) at 25°C and 200 rpm for aeration.…”

Section: Methodsmentioning

confidence: 99%

“…Bacterial ethyl acetate extracts were evaporated to dryness in a vacuum, redissolved in 10 times less methanol-water (1:1), and analyzed on two different columns; 1 l was injected in an Agilent 1100 high-pressure liquid chromatograph (HPLC) equipped with either a Phenomenex (Torrance, Calif.) Luna II C 18 , 3-m particle, 50-by 2-mm column and a Phenomenex 2-mm C 18 SecurityGuard precolumn or a Phenomenex Polar RP Phenyl, 3-m particle, 150-by 2-mm column and a Phenomenex 2-mm Polar RP SecurityGuard precolumn. The HPLC was coupled to an LCT orthogonal time-of-flight mass spectrometer (MS; Micromass, Manchester, United Kingdom), and equipped with a Z-spray electrospray ionization (ESI) source and a LockSpray probe (using leucine encephalin as lock mass) (41).…”

Section: Confirmation Of Ahls By Hplc-esimentioning

confidence: 99%

Nonbioluminescent Strains ofPhotobacterium phosphoreumProduce the Cell-to-Cell Communication SignalN-(3-Hydroxyoctanoyl)homoserine Lactone

Flodgaard¹,

Dalgaard²,

Andersen³

et al. 2005

Bioluminescence is a common phenotype in marine bacteria, such as Vibrio and Photobacterium species, and can be quorum regulated by N-acylated homoserine lactones (AHLs). We extracted a molecule that induced a bacterial AHL monitor (Agrobacterium tumefaciens NT1 [pZLR4]) from packed cod fillets, which spoil due to growth of Photobacterium phosphoreum. Interestingly, AHLs were produced by 13 nonbioluminescent strains of P. phosphoreum isolated from the product. Of 177 strains of P. phosphoreum (including 18 isolates from this study), none of 74 bioluminescent strains elicited a reaction in the AHL monitor, whereas 48 of 103 nonbioluminescent strains did produce AHLs. AHLs were also detected in Aeromonas spp., but not in Shewanella strains. Thin-layer chromatographic profiles of cod extracts and P. phosphoreum culture supernatants identified a molecule similar in relative mobility (R f value) and shape to N-(3-hydroxyoctanoyl)homoserine lactone, and the presence of this molecule in culture supernatants from a nonbioluminescent strain of P. phosphoreum was confirmed by high-performance liquid chromatography-positive electrospray high-resolution mass spectrometry. Bioluminescence (in a non-AHL-producing strain of P. phosphoreum) was strongly up-regulated during growth, whereas AHL production in a nonbioluminescent strain of P. phosphoreum appeared constitutive. AHLs apparently did not influence bioluminescence, as the addition of neither synthetic AHLs nor supernatants delayed or reduced this phenotype in luminescent strains of P. phosphoreum. The phenotypes of nonbioluminescent P. phosphoreum strains regulated by AHLs remains to be elucidated.Photobacterium phosphoreum is a marine psychrotolerant bacterium commonly found free living in temperate waters or in high cell densities in fish gastrointestinal tracts and in specific light organelles in deep-sea fish (30,47,48). P. phosphoreum is a bioluminescent bacterium (17, 46, 48) which can also be found in dim and dark versions; loss of bioluminescence among bioluminescent species is frequently observed (16,30,31,53). It is unknown if all nonbioluminescent P. phosphoreum strains have ever been able to produce light, although it has been shown that at least part of the essential lux genes is present in both the luminous and nonluminous strains (17). P. phosphoreum belongs to the Vibrionaceae, which harbor several bioluminescent species, including Vibrio fischeri and Vibrio harveyi. These two species regulate bioluminescence through a circuit popularly referred to as quorum sensing (22,50,54). In brief, quorum sensing involves the production and release of communication molecules, which accumulate in the surrounding environment and allow the bacteria to coregulate specific phenotypes. The communication molecules regulating bioluminescence in V. fischeri are N-acylated homoserine lactones (AHLs). Quorum sensing enables V. fischeri to up-regulate bioluminescence when situated in its symbiont Euprymna scolopes and abolish light production when in the free-living state in t...