scholarly journals Abundant proteins contribute strongly to noise in the growth rate of optimally growing bacteria

2020 ◽  
Author(s):  
L.H.J. Krah ◽  
R. Hermsen
Keyword(s):  
Growth Rate ◽  
Low Noise ◽  
Cellular Growth ◽  
Bacterial Cells ◽  
Noise Levels ◽  
Physiological Variables ◽  
Stochastic Fluctuations ◽  
Two Factors ◽  
The Mean ◽  
Related Proteins

AbstractIn bacterial cells, protein expression is a highly stochastic process. At the same time, physiological variables such as the cellular growth rate also fluctuate significantly. A common intuition is that, due to their relatively high noise amplitudes, proteins with a low mean expression level are the most important causes of these fluctuations on a larger, physiological scale. Noise in highly expressed proteins, whose stochastic fluctuations are relatively small, is often ignored. In this work, we challenge this intuition by developing a theory that predicts the contribution of a protein’s expression noise to the noise in the instantaneous, cellular growth rate. Using mathematical analysis, we decomposed the contribution of each protein into two factors: the noise amplitude of the protein, and the sensitivity of the growth rate to fluctuations in that protein’s concentration. Next, we incorporated evolution, which has shaped the mean abundances of growth-related proteins to optimise the growth rate, causing protein abundances, but also cellular sensitivities to be non-random. We show that in cells that grow optimally fast, the growth rate is most sensitive to fluctuations in highly abundant proteins. This causes such proteins to overall contribute strongly to the noise in the growth-rate, despite their low noise levels. The results are confirmed in a stochastic toy model of cellular growth.

scholarly journals The effect of natural selection on the propagation of protein expression noise to bacterial growth

2021 ◽  
Vol 17 (7) ◽  
pp. e1009208
Author(s):  
Laurens H. J. Krah ◽  
Rutger Hermsen
Keyword(s):  
Growth Rate ◽  
Natural Selection ◽  
Protein Expression ◽  
Low Noise ◽  
Cellular Growth ◽  
Expression Level ◽  
Bacterial Cells ◽  
Data Set ◽  
Physiological Variables ◽  
Expression Noise

In bacterial cells, protein expression is a highly stochastic process. Gene expression noise moreover propagates through the cell and adds to fluctuations in the cellular growth rate. A common intuition is that, due to their relatively high noise amplitudes, proteins with a low mean expression level are the most important drivers of fluctuations in physiological variables. In this work, we challenge this intuition by considering the effect of natural selection on noise propagation. Mathematically, the contribution of each protein species to the noise in the growth rate depends on two factors: the noise amplitude of the protein’s expression level, and the sensitivity of the growth rate to fluctuations in that protein’s concentration. We argue that natural selection, while shaping mean abundances to increase the mean growth rate, also affects cellular sensitivities. In the limit in which cells grow optimally fast, the growth rate becomes most sensitive to fluctuations in highly abundant proteins. This causes abundant proteins to overall contribute strongly to the noise in the growth rate, despite their low noise levels. We further explore this result in an experimental data set of protein abundances, and test key assumptions in an evolving, stochastic toy model of cellular growth.

scholarly journals Structure of growing microtubule ends: two-dimensional sheets close into tubes at variable rates.

1995 ◽  
Vol 129 (5) ◽  
pp. 1311-1328 ◽  
Author(s):  
D Chrétien ◽  
S D Fuller ◽  
E Karsenti
Keyword(s):  
Electron Microscopy ◽  
Growth Rate ◽  
Conformational Changes ◽  
Two Dimensional ◽  
Closure Rate ◽  
Cryo Electron Microscopy ◽  
Stochastic Fluctuations ◽  
Microtubule Growth ◽  
The Mean ◽  
Dependent Growth

Observation of microtubule growth at different rates by cryo-electron microscopy reveals that the ends range from blunt to long, gently curved sheets. The mean sheet length increases with the growth rate while the width of the distributions increases with the extent of assembly. The combination of a concentration dependent growth rate of the tubulin sheet with a variable closure rate of the microtubule cylinder, results in a model in which stochastic fluctuations in sheet length and tubulin conformation confine GTP-tubulins to microtubule ends. We propose that the variability of microtubule growth rate observed by video microscopy (Gildersleeve, R. F., A. R. Cross, K. E. Cullen, A. P. Fagen, and R. C. Williams. 1992. J. Biol. Chem. 267: 7995-8006, and this study) is due to the variation in the rate of cylinder closure. The curvature of the sheets at the end of growing microtubules and the small oligomeric structures observed at the end of disassembling microtubules, indicate that tubulin molecules undergo conformational changes both during assembly and disassembly.

COLLECTIVE DYNAMICS IN AN ENSEMBLE OF GLOBALLY COUPLED FHN SYSTEMS

2005 ◽  
Vol 05 (02) ◽  
pp. L299-L304 ◽  
Author(s):  
X. SAILER ◽  
M. ZAKS ◽  
L. SCHIMANSKY-GEIER
Keyword(s):  
Equations Of Motion ◽  
Gaussian White Noise ◽  
Period Doubling ◽  
Low Noise ◽  
Small Scale ◽  
Correlated Noise ◽  
Noise Levels ◽  
Uncorrelated Noise ◽  
The Mean ◽  
Correlation Strength

We examine an ensemble of globally coupled FitzHugh–Nagumo systems with Gaussian, white noise. In the case of spatially uncorrelated noise, at high and at low noise levels the mean of the ensemble is steady. In between it exhibits a complex behavior. Depending on the noise intensity we find small scale oscillations, period doubling, chaos, and spiking. We derive equations of motion for the cumulants of the ensemble distribution. The results of the cumulant dynamics analysis are in good qualitative agreement with results from the Langevin dynamics. Once we additionally apply correlated noise but keep the sum of both noise intensities constant, the mean starts spiking where it did not spike with uncorrelated noise. For increasing correlation strength a minimum of the coefficient of variation appears.

Determination of the Probability Distribution of the Mean Sound Level

2010 ◽  
Vol 35 (4) ◽  
pp. 543-550 ◽  
Author(s):  
Wojciech Batko ◽  
Bartosz Przysucha
Keyword(s):  
Probability Distribution ◽  
Probability Distribution Function ◽  
Mean Value ◽  
Sound Level ◽  
Function Form ◽  
Noise Levels ◽  
Logarithmic Mean ◽  
The Mean ◽  
Estimation Of Uncertainty

AbstractAssessment of several noise indicators are determined by the logarithmic mean <img src="/fulltext-image.asp?format=htmlnonpaginated&src=P42524002G141TV8_html\05_paper.gif" alt=""/>, from the sum of independent random resultsL1;L2; : : : ;Lnof the sound level, being under testing. The estimation of uncertainty of such averaging requires knowledge of probability distribution of the function form of their calculations. The developed solution, leading to the recurrent determination of the probability distribution function for the estimation of the mean value of noise levels and its variance, is shown in this paper.

The incidence of central serous chorioretinopathy in the population of Kazan: Results of a retrospective epidemiological analysis for 2009–2018

2020 ◽  
pp. 30-33
Author(s):  
D. R. Аgliullin ◽  
G. R. Khasanova ◽  
E. A. Abdulaeva ◽  
S. T. Agliullina ◽  
A. N. Amirov ◽  
...  
Keyword(s):  
Growth Rate ◽  
Central Serous Chorioretinopathy ◽  
Morbidity Rate ◽  
Seasonal Prevalence ◽  
Upward Trend ◽  
Epidemiological Analysis ◽  
Significant Upward Trend ◽  
Track Record ◽  
Minimum Age ◽  
The Mean

Objective: To assess the incidence of central serous chorioretinopathy (CSC) through the example of a large industrial Russian city.Methods: A retrospective analysis of CSC of Kazan population for 2009–2018 has been done.Results: From 2019 to 2018, 831 new cases of CSC were registered in Kazan. A statistically significant upward trend with growth rate 105.2% and accession rate 5.2% was typical for the annual track record. The mean age of patients was 50 years, the minimum age was 14 years, the maximum age was 87 years. A statistically significant upward trend was detected in track record of incidence in groups of 30–39-year-old and 40–49-year-old. Seasonal increase of the incidence was recorded in February, March, April, October, and November.Conclusions: The upward trend and seasonal prevalence are typical for longterm morbidity of CSC in Kazan. The highest morbidity rate of CSC and statistically significant upward trend of its incidence in track record were recorded in the age of 30–39. 

Continuous Precipitation of Lead Iodide

1994 ◽  
Vol 59 (7) ◽  
pp. 1503-1510
Author(s):  
Stanislav Žáček ◽  
Jaroslav Nývlt
Keyword(s):  
Growth Rate ◽  
Particle Size ◽  
Crystal Size ◽  
Crystal Growth Rate ◽  
Laboratory Model ◽  
Lead Iodide ◽  
Secondary Nucleation ◽  
Equimolar Ratio ◽  
Continuous Precipitation ◽  
The Mean

Lead iodide was precipitated from aqueous solutions of 0.015 - 0.1 M Pb(NO3)2 and 0.03 - 0.2 M KI in the equimolar ratio using a laboratory model of a stirred continuous crystallizer at 22 °C. After reaching the steady state, the PbI2 crystal size distribution was measured sedimentometrically and the crystallization kinetics was evaluated based on the mean particle size. Both the linear crystal growth rate and the nucleation rate depend on the specific output of the crystallizer. The system crystallization constant either points to a significant effect of secondary nucleation by the mechanism of contact of the crystals with the stirrer blade, or depends on the concentrations of the components added due to the micromixing mechanism.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples

2019 ◽  
Vol 73 (12) ◽  
pp. 1436-1450 ◽  
Author(s):  
Fabiola León-Bejarano ◽  
Martin O. Méndez ◽  
Miguel G. Ramírez-Elías ◽  
Alfonso Alba
Keyword(s):  
Empirical Mode Decomposition ◽  
Raman Spectra ◽  
Biological Samples ◽  
Biological Material ◽  
Noise Levels ◽  
Synthetic Material ◽  
High Noise ◽  
Mode Decomposition ◽  
Mean Filter ◽  
The Mean

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA ([Formula: see text]) outperforms VRA ([Formula: see text]) for moderate to high noise levels whereas mVRA outperformed EMD ([Formula: see text]) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results ([Formula: see text]. For Raman spectra of synthetic material, good performance of the three methods ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ([Formula: see text] for VRA, [Formula: see text] for EMD, and [Formula: see text] for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

scholarly journals Noise in Otolaryngology – Head and Neck Surgery operating rooms: a systematic review

2021 ◽  
Vol 50 (1) ◽  
Author(s):  
Gianluca Sampieri ◽  
Amirpouyan Namavarian ◽  
Marc Levin ◽  
Justine Philteos ◽  
Jong Wook Lee ◽  
...  
Keyword(s):  
Systematic Review ◽  
Head And Neck ◽  
Patient Outcomes ◽  
Noise Level ◽  
Neck Surgery ◽  
Operating Rooms ◽  
Head And Neck Surgery ◽  
Noise Levels ◽  
Surgical Performance ◽  
The Mean

Abstract Objective Noise in operating rooms (OR) can have negative effects on both patients and surgical care workers. Noise can also impact surgical performance, team communication, and patient outcomes. Such implications of noise have been studied in orthopedics, neurosurgery, and urology. High noise levels have also been demonstrated in Otolaryngology-Head and Neck Surgery (OHNS) procedures. Despite this, no previous study has amalgamated the data on noise across all OHNS ORs to determine how much noise is present during OHNS surgeries. This study aims to review all the literature on noise associated with OHNS ORs and procedures. Methods Ovid Medline, EMBASE Classic, Pubmed, SCOPUS and Cochrane databases were searched following PRISMA guidelines. Data was collected on noise measurement location and surgery type. Descriptive results and statistical analysis were completed using Stata. Results This search identified 2914 articles. Final inclusion consisted of 22 studies. The majority of articles analyzed noise level exposures during mastoid surgery (18/22, 82%). The maximum noise level across all OHNS ORs and OHNS cadaver studies were 95.5 a-weighted decibels (dBA) and 106.6 c-weighted decibels (dBC), respectively (P = 0.2068). The mean noise level across all studies was significantly higher in OHNS cadaver labs (96.9 dBA) compared to OHNS ORs (70.1 dBA) (P = 0.0038). When analyzed together, the mean noise levels were 84.9 dBA. Conclusions This systematic review demonstrates that noise exposure in OHNS surgery exceeds safety thresholds. Further research is needed to understand how noise may affect team communication, surgical performance and patient outcomes in OHNS ORs. Graphical abstract

scholarly journals Precise regulation of the relative rates of surface area and volume synthesis in bacterial cells growing in dynamic environments

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Handuo Shi ◽  
Yan Hu ◽  
Pascal D. Odermatt ◽  
Carlos G. Gonzalez ◽  
Lichao Zhang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Time Delay ◽  
Surface Area ◽  
Environmental Changes ◽  
Volume Ratio ◽  
Dynamic Environments ◽  
Bacterial Cells ◽  
Bacterial Growth Rate ◽  
State Size ◽  
Insight Into

AbstractThe steady-state size of bacterial cells correlates with nutrient-determined growth rate. Here, we explore how rod-shaped bacterial cells regulate their morphology during rapid environmental changes. We quantify cellular dimensions throughout passage cycles of stationary-phase cells diluted into fresh medium and grown back to saturation. We find that cells exhibit characteristic dynamics in surface area to volume ratio (SA/V), which are conserved across genetic and chemical perturbations as well as across species and growth temperatures. A mathematical model with a single fitting parameter (the time delay between surface and volume synthesis) is quantitatively consistent with our SA/V experimental observations. The model supports that this time delay is due to differential expression of volume and surface-related genes, and that the first division after dilution occurs at a tightly controlled SA/V. Our minimal model thus provides insight into the connections between bacterial growth rate and cell shape in dynamic environments.

scholarly journals Effect of genotype, age of hens and K/k allele on eggshell quality

2011 ◽  
Vol 56 (No. 5) ◽  
pp. 242-249 ◽  
Author(s):  
Z. Ledvinka ◽  
L. Zita ◽  
M. Hubený ◽  
E. Tůmová ◽  
M. Tyller ◽  
...  
Keyword(s):  
Growth Rate ◽  
Egg Production ◽  
Statistical Significance ◽  
Live Weight ◽  
Shape Index ◽  
Feed Mixture ◽  
Eggshell Quality ◽  
Feather Growth ◽  
Two Factors ◽  
The Relationship

We assessed the influence of the particular genotype, age of layers, feather growth-rate gene, and their mutual interactions on selected indicators of eggshell quality in six groups of hens of the laying type Dominant. The following genotypes were examined in the experiment: Barred Plymouth Rock, Dominant BPR 951 (K) strain, slow-feathering; Barred Plymouth Rock, Dominant BPR 901 (k) strain, fast-feathering; Blue Plymouth Rock, Dominant BLPR 954 (K) strain, slow-feathering; Blue Plymouth Rock, Dominant BLPR 894 (k) strain, fast-feathering; crossbreds of the above strains in the F<sub>1</sub> generation Dominant D 107 blue (K), slow-feathering and Dominant D 107 blue (k), fast-feathering. The layers were fed a feed mixture NP1 (16.64 % CP) from the 20<sup>th</sup> week of age and a feed mixture NP2 (15.02% CP) from the 42<sup>nd</sup> week. Husbandry conditions met the regular requirements of laying hens. Egg production and live weight of hens were monitored for the duration of the experiment (12 months). Eggshell quality was examined at the layers' age of 27, 35 and 56 weeks. The average hen-day egg production for the duration of the experiment (12 months) was not significantly influenced by the particular genotype or the feather growth-rate gene. The varying representation of the feather growth-rate gene significantly (P &le; 0.001) influenced the live weight; similarly, the relationship between the genotype and the representation of K/k alleles was significant. The average egg weight was influenced statistically significantly (P &le; 0.001) by the age of hens, their genotype (P &le; 0.05), feather growth-rate gene (P &le; 0.001), and the relationship between the age and genotype (P &le; 0.001). The age of hens, genotype, and the interaction of these two factors affected the egg shape index, as did the incidence of the feather growth-rate gene within the population (with a statistical significance of P &le; 0.001). The age, genotype and the feather growth-rate gene incidence within the population also significantly affected the eggshell quality indicators. In the eggshell to egg ratio, eggshell thickness and strength, an interaction was determined between the age of hens and their particular genotype. The eggshell colour was also significantly (P &le; 0.001) affected by hens' age, genotype (P &le; 0.001), as well as by the feather growth-rate gene (P &le; 0.001). No significant interaction between the age and the genotype was found for this indicator.

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