General calibration of microbial growth in microplate readers

Stevenson, Keiran; McVey, Alexander; Clark, Ivan B. N.; Swain, Peter S.; Pilizota, Teuta

doi:10.1038/srep38828

Cited by 287 publications

(274 citation statements)

References 30 publications

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“…Since model construction is aided by absolute abundance information (Bucci et al , ; Widder et al , ), the total biomass of the communities was monitored approximately every 30 min using absorbance at 600 nm (OD600). Cellular traits such as cell adhesion, size, and shape can influence OD600 measurements (Stevenson et al , ). In addition, counting of colony‐forming units (CFU) is biased by cell adhesion, dormant sub‐populations, growth selection on solid vs. liquid media, and growth stage (Jansson & Prosser, ; Volkmer & Heinemann, ; Ou et al , ).…”

Section: Resultsmentioning

confidence: 99%

Deciphering microbial interactions in synthetic human gut microbiome communities

Venturelli

Carr

Fisher

et al. 2018

Molecular Systems Biology

427

420

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The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities.

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Section: Resultsmentioning

confidence: 99%

Deciphering microbial interactions in synthetic human gut microbiome communities

Venturelli

Carr

Fisher

et al. 2018

Molecular Systems Biology

427

420

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“…For Mie scattering calculations it is necessary to determine the real refractive index (n r ) and the imaginary refractive index (n i ) at the UVC wavelength of 254 nm for particles in the size range of interest, which is about 0·2–3 μm. Escherichia coli has a refractive index of about 1·4 at about 520–589 nm, as do several other bacteria including Staphylococcus aureus and Enterococcus faecalis (Stevenson et al ). Water has a refractive index of 1·333 at 550 nm and about 1·36 at 254 nm, which gives a ratio of 1·36/1·33 = 1·02 (Hale and Querry ).…”

Section: Methodsmentioning

confidence: 99%

“…We adopt this value of the real refractive index, n r = 1·43, for all bacteria and viruses. Spores of Bacillus cereus and Bacillus megaterium have a refractive index of about 1·53 at 542 nm (Stevenson et al ). Scaling the visible light refractive index of 1·53 for spores we get a real refractive index of n r = 1·56.…”

Section: Methodsmentioning

confidence: 99%

The cluster model of ultraviolet disinfection explains tailing kinetics

et al. 2019

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Aims To develop a new mathematical model derived from first principles to define the kinetics of ultraviolet disinfection and to explain the phenomenon known as tailing. The theory presented interprets tailing as the result of photoprotection due to cumulative Mie scattering effects in clustered populations of micro‐organisms. Methods and Results Mie scattering effects at ultraviolet wavelengths are used to compute a shielding constant for each micro‐organism based on the average projected diameter. An intrinsic rate constant, hypothesized to be a characteristic property of the microbial genome alone, is computed. The cluster model is fitted to tailing data from 30 ultraviolet inactivation studies and results are compared with the classic two stage multihit model. Conclusions The cluster model demonstrates a statistically significant improvement in the mean adjusted R2 values of the tested data sets (P < 0·0001). Tailing in survival curves is the direct consequence of the Gaussian distribution of cluster sizes and the intrinsic rate constant is a real and critical parameter that defines ultraviolet susceptibility. Significance and Impact of the Study The ultraviolet dose–response behaviour of micro‐organisms can now be explained in terms of parameters that have physical meaning and provide deep insight into the disinfection process.

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“…Turning to experimental measurements, we fit optical densities, which are proportional to the number of cells if properly calibrated1112, and show that we can infer growth rates for two cases that cannot be easily described by parametric approaches3. The first exhibits a diauxic shift with two distinct phases of growth and the second shows an exceptionally long lag (Fig.…”

Section: Resultsmentioning

confidence: 99%

Inferring time derivatives including cell growth rates using Gaussian processes

et al. 2016

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Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.

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General calibration of microbial growth in microplate readers

Cited by 287 publications

References 30 publications

Deciphering microbial interactions in synthetic human gut microbiome communities

Deciphering microbial interactions in synthetic human gut microbiome communities

The cluster model of ultraviolet disinfection explains tailing kinetics

Inferring time derivatives including cell growth rates using Gaussian processes

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