The growth rate of E. coli in relation to temperature, quinine and coenzyme

Johnson, Frank H.; Lewin, Isaac

doi:10.1002/jcp.1030280104

Cited by 143 publications

(129 citation statements)

References 20 publications

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“…The 40 • C traces are always higher than the 30 • C traces and them, always higher than the 20 • C traces. This behavior agrees with the fundamental findings of Johnson and Lewin [23] that attribute higher cell division rates in higher temperatures. Secondly, there is a statistical observation presented in Table 3 that generally, higher initial numbers lead to larger percentile increases of population, when the critical temperature is reached.…”

Section: Tablesupporting

confidence: 92%

“…This is attributable to the increased degradation of the vital components of the cell, by Table 3 Percentile change of bacterial concentration after 4 h of treatment in absence of solar light. the decomposition mechanisms characterized many decades ago [23,26] (Table 4). As far as the efficiency of the process is concerned, in terms of removal percentage, we notice the variation in Fig.…”

Section: Tablementioning

confidence: 99%

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The antagonistic and synergistic effects of temperature during solar disinfection of synthetic secondary effluent

Giannakis

Darakas

Escalas-Cañellas

et al. 2014

Journal of Photochemistry and Photobiology A: Chemistry

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a b s t r a c tA 4-factor, multilevel, full factorial design of 240 experiments was performed in order to investigate the effect of temperature on the inactivation efficiency of spiked Escherichia coli in simulated solar disinfection of a synthetic secondary effluent. The initial population of the microorganisms was 10 3 , 10 4 , 10 5 and 10 6 CFU/mL, the exposure time 1, 2, 3 and 4 h, the treatment temperature 20, 30, 40, 50 and 60• C and the sunlight intensity 0, 800 and 1200 W/m 2 . Radical changes in bacterial behavior, process efficiency and remaining populations were observed, while treating effluents in discreet temperatures. Elevating treatment temperature from 20 to 40• C drastically impaired disinfection. Thermal inactivation with no regrowth predominated at 50• C and total inactivation of microorganisms was observed at 60• C in nonirradiated samples. Irradiation at 800 and 1200 W/m 2 much increased inactivation efficiency, especially at 50 and 60• C, proving sensitive light-temperature synergy at those temperatures. Total inactivation was achieved within 4 h under a range of treatment conditions, including all samples at 1200 W/m 2 , or 60• C samples at 800 W/m 2 . Also, 99.9-100% efficiencies and final population below 1000 CFU/100 mL were obtained at 800 W/m 2 and temperatures of 50 • C and above. Treatment time, temperature and intensity are the critical parameters for the disinfection process, while initial population is insignificant for removal efficiency. An explanation of the mechanism of the process as well as a general linear model predicting the outcome of the experiments is also suggested.

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Section: Tablesupporting

confidence: 92%

Section: Tablementioning

confidence: 99%

The antagonistic and synergistic effects of temperature during solar disinfection of synthetic secondary effluent

Giannakis

Darakas

Escalas-Cañellas

et al. 2014

Journal of Photochemistry and Photobiology A: Chemistry

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“…Consequently, a variety of mathematical functions have been used to fit experimental performance data (Angilletta, 2006). For biochemical and physiological rate data, the ascending leg of the performance curve is often assumed to result from thermodynamic effects that increase reaction rates with increasing temperature (according to the Boltzmann-Arrhenius equation), while deceleration near the peak and on the descending leg is assumed to result from destabilizing effects of high temperatures on ratelimiting enzymes (Dell et al, 2011;Johnson and Lewin, 1946;Knies and Kingsolver, 2010;Ratkowsky et al, 2005). In aquatic organisms, recent attention has focused on the roles of limitations in oxygen-carrying capacity and resulting oxidative stress in setting thermal performance bounds (Pörtner, 2002(Pörtner, , 2010.…”

Section: Introductionmentioning

confidence: 99%

Thermal variation, thermal extremes and the physiological performance of individuals

Dowd

King

Denny

2015

Journal of Experimental Biology

219

211

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In this review we consider how small-scale temporal and spatial variation in body temperature, and biochemical/physiological variation among individuals, affect the prediction of organisms' performance in nature. For 'normal' body temperatures -benign temperatures near the species' mean -thermal biology traditionally uses performance curves to describe how physiological capabilities vary with temperature. However, these curves, which are typically measured under static laboratory conditions, can yield incomplete or inaccurate predictions of how organisms respond to natural patterns of temperature variation. For example, scale transition theory predicts that, in a variable environment, peak average performance is lower and occurs at a lower mean temperature than the peak of statically measured performance. We also demonstrate that temporal variation in performance is minimized near this new 'optimal' temperature. These factors add complexity to predictions of the consequences of climate change. We then move beyond the performance curve approach to consider the effects of rare, extreme temperatures. A statistical procedure (the environmental bootstrap) allows for longterm simulations that capture the temporal pattern of extremes (a Poisson interval distribution), which is characterized by clusters of events interspersed with long intervals of benign conditions. The bootstrap can be combined with biophysical models to incorporate temporal, spatial and physiological variation into evolutionary models of thermal tolerance. We conclude with several challenges that must be overcome to more fully develop our understanding of thermal performance in the context of a changing climate by explicitly considering different forms of small-scale variation. These challenges highlight the need to empirically and rigorously test existing theories.

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“…To build a microbial model, the substrate kinetics is fundamental as it describes the rate at which microbes would take up a substrate and represents the first step towards describing how microbes would decompose the soil organic matter. Under the assumption that a single "master reaction" limits the growth of microbes (Johnson and Lewin, 1946), the substrate kinetics even completely determines the microbial dynamics as done in many models (e.g., the Monod model). Among the many mathematical formulations of substrate kinetics (see Tang and Riley, 2013 for a review), the MichaelisMenten (MM) kinetics is often used because it has succeeded in many applications ever since its creation in the early 20th century (Michaelis and Menten, 1913).…”

Section: Introductionmentioning

confidence: 99%

On the relationships between the Michaelis–Menten kinetics, reverse Michaelis–Menten kinetics, equilibrium chemistry approximation kinetics, and quadratic kinetics

Tang

2015

Geosci. Model Dev.

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Abstract. The Michaelis-Menten kinetics and the reverse Michaelis-Menten kinetics are two popular mathematical formulations used in many land biogeochemical models to describe how microbes and plants would respond to changes in substrate abundance. However, the criteria of when to use either of the two are often ambiguous. Here I show that these two kinetics are special approximations to the equilibrium chemistry approximation (ECA) kinetics, which is the first-order approximation to the quadratic kinetics that solves the equation of an enzyme-substrate complex exactly for a single-enzyme and single-substrate biogeochemical reaction with the law of mass action and the assumption of a quasi-steady state for the enzyme-substrate complex and that the product genesis from enzyme-substrate complex is much slower than the equilibration between enzymesubstrate complexes, substrates, and enzymes. In particular, I show that the derivation of the Michaelis-Menten kinetics does not consider the mass balance constraint of the substrate, and the reverse Michaelis-Menten kinetics does not consider the mass balance constraint of the enzyme, whereas both of these constraints are taken into account in deriving the equilibrium chemistry approximation kinetics. By benchmarking against predictions from the quadratic kinetics for a wide range of substrate and enzyme concentrations, the Michaelis-Menten kinetics was found to persistently underpredict the normalized sensitivity ∂ ln v/∂ ln k + 2 of the reaction velocity v with respect to the maximum product genesis rate k T , indicating that ECAbased models will be more calibratable if the modeled processes do obey the law of mass action. Since the equilibrium chemistry approximation kinetics includes advantages from both the Michaelis-Menten kinetics and the reverse Michaelis-Menten kinetics and it is applicable for almost the whole range of substrate and enzyme abundances, land biogeochemical modelers therefore no longer need to choose when to use the Michaelis-Menten kinetics or the reverse Michaelis-Menten kinetics. I expect that removing this choice ambiguity will make it easier to formulate more robust and consistent land biogeochemical models.

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The growth rate of E. coli in relation to temperature, quinine and coenzyme

Cited by 143 publications

References 20 publications

The antagonistic and synergistic effects of temperature during solar disinfection of synthetic secondary effluent

The antagonistic and synergistic effects of temperature during solar disinfection of synthetic secondary effluent

Thermal variation, thermal extremes and the physiological performance of individuals

On the relationships between the Michaelis–Menten kinetics, reverse Michaelis–Menten kinetics, equilibrium chemistry approximation kinetics, and quadratic kinetics

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