scholarly journals Predicting microbial growth in a mixed culture from growth curve data

2019 ◽  
Vol 116 (29) ◽  
pp. 14698-14707 ◽  
Author(s):  
Yoav Ram ◽  
Eynat Dellus-Gur ◽  
Maayan Bibi ◽  
Kedar Karkare ◽  
Uri Obolski ◽  
...  
Keyword(s):  
Mixed Culture ◽  
Microbial Growth ◽  
Growth Curves ◽  
Growth Cycle ◽  
Relative Fitness ◽  
Accurate Estimation ◽  
Environmental Perturbations ◽  
Fitness Consequence ◽  
Culture Growth ◽  
Growth Differences

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current “gold standard” for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments withEscherichia coliand demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

scholarly journals Predicting microbial relative growth in a mixed culture from growth curve data

2015 ◽  
Author(s):  
Yoav Ram ◽  
Eynat Dellus-Gur ◽  
Maayan Bibi ◽  
Uri Obolski ◽  
Judith Berman ◽  
...  
Keyword(s):  
Mixed Culture ◽  
Growth Curve ◽  
Growth Curves ◽  
Relative Fitness ◽  
Model Organisms ◽  
Accurate Estimation ◽  
Relative Growth ◽  
E Coli ◽  
Culture Growth ◽  
Unique Markers

AbstractEstimates of microbial fitness from growth curves are inaccurate. Rather, competition experiments are necessary for accurate estimation. But competition experiments require unique markers and are difficult to perform with isolates derived from a common ancestor or non-model organisms. Here we describe a new approach for predicting relative growth of microbes in a mixed culture utilizing mono- and mixed culture growth curve data. We validated this approach using growth curve and competition experiments withE. coli. Our approach provides an effective way to predict growth in a mixed culture and infer relative fitness. Furthermore, by integrating several growth phases, it provides an ecological interpretation for microbial fitness.

scholarly journals Detection of mixed-culture growth in the total biomass data by wavelet transforms

2010 ◽  
Vol 8 (02) ◽  
Author(s):  
H.C. Rosu ◽  
J.S. Murguía ◽  
V. Ibarra-Junquera
Keyword(s):  
Wavelet Transform ◽  
Mixed Culture ◽  
Microbial Growth ◽  
Physical Parameters ◽  
Logarithmic Scale ◽  
Total Biomass ◽  
Growth Processes ◽  
Microbial Species ◽  
Hölder Exponents ◽  
Culture Growth

We have shown elsewhere that the presence of mixed-culture growth of microbial species in fermentation processes can be detected with high accuracy by employing the wavelet transform. This is achieved because the crosses in the different growth processes contributing to the total biomass signal appear as singularities that are very well evidenced through their singularity cones in the wavelet transform; however, we used very simple two-species cases.In this work, we extend the wavelet method to a more complicated illustrative fermentation case of three microbial species for which we employ several wavelets of different number of vanishing moments in order to eliminate possible numerical artifacts. Working in this way allows filtering in a more precise way the numerical values of the Hölder exponents; therefore, we were able to determine the characteristic Hölder exponents for the corresponding crossing singularities of the microbial growth processes and their stability logarithmic scale ranges up to the first decimal in the value of the characteristic exponents. Since calibrating the mixed microbial growth by means of their Hölder exponents could have potential industrial applications, the dependence of the Hölder exponents on the kinetic and physical parameters of the growth models remains as a future experimental task.

Microbial Growth Response to Hydrogel Encapsulated Quantum Dot Nanospheres

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.

EFFECT OF HUMIC AND FULVIC ACIDS ON MICROBIAL GROWTH

2021 ◽  
Author(s):  
Milanka Radulovic ◽  
◽  
Svetlana Mitrovski
Keyword(s):  
Humic Acid ◽  
Humic Substances ◽  
Minimal Medium ◽  
Microbial Growth ◽  
Growth Curves ◽  
Fulvic Acids ◽  
Humic And Fulvic Acids ◽  
Staphyloccocus Aureus ◽  
Low Ionic Strength ◽  
Strength Medium

Peat is a natural substrate for growth of microorganisms because it is rich in compounds that microorganisms can use as sources of carbon, nitrogen and growth factors. Peat originating from Vlasina lake in Eastern Serbia is especially rich in organic matter. The content of humic substances (humic acid, fulvic acid and humine) is almost twice that found in other peat-rich regions of similar origin and geochemical age. Humic and fluvic acids are known to promote microbial growth. In this work, humic and fulvic acids were first extracted from Vlasina lake peat and then added to minimal medium (synthetic, low ionic strength medium). The humic substances were added separately and combined in a 1:1 ratio by mass to study their individual and combined effect on microbial growth of Escherichia coli ATCC 25922 (Gr–), Staphyloccocus aureus (Gr+) i Aureobasidium pullulans, strain CH-1. The microbial growth was measured microspectrophotometrically over a 24-hour period and growth curves were obtained for a range of acid concentrations between 25 µg cm-3 and 100 µg cm-3. It was found that both humic and fulvic acids promote the growth of all three microorganisms by up to a maximum of 40%-80% the extent of which varied with the concentration of the acid and the identity of the microorganism. In general, humic acid was found to result in higher microbial growth (at highest concentrations, up to ~80% for all three microbial species).

scholarly journals A Real-Time Multiplexed Microbial Growth Intervalometer for Capturing High-Resolution Growth Curves

2019 ◽  
Vol 10 ◽  
Author(s):  
David C. Vuono ◽  
Bruce Lipp ◽  
Carl Staub ◽  
Evan Loney ◽  
Zoë R. Harrold ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Microbial Growth ◽  
Growth Curves

On the lag phase and initial decline of microbial growth curves

2007 ◽  
Vol 244 (3) ◽  
pp. 511-517 ◽  
Author(s):  
George T. Yates ◽  
Thomas Smotzer
Keyword(s):  
Microbial Growth ◽  
Growth Curves ◽  
Lag Phase

scholarly journals Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages

2019 ◽  
Vol 11 (13) ◽  
pp. 1584 ◽  
Author(s):  
Yang Chen ◽  
Won Suk Lee ◽  
Hao Gan ◽  
Natalia Peres ◽  
Clyde Fraisse ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection System ◽  
Growth Cycle ◽  
Aerial Images ◽  
Accurate Estimation ◽  
Yield Prediction ◽  
Distribution Maps ◽  
Detection And Counting

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.

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