scholarly journals Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

2017 ◽  
Author(s):  
Samuel H. Friedman ◽  
Paul Macklin
Keyword(s):  
High Throughput ◽  
Growth Rates ◽  
Exponential Growth ◽  
Cell Counting ◽  
Biological Variability ◽  
Technical Variability ◽  
Line Identification ◽  
Cell Line Identification ◽  
Synthetic Datasets ◽  
The Impact

AbstractHigh-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

scholarly journals Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19

2020 ◽  
Author(s):  
G. Livadiotis
Keyword(s):  
Growth Rate ◽  
Statistical Analysis ◽  
Growth Rates ◽  
Exponential Growth ◽  
Environmental Temperature ◽  
Exponential Growth Phase ◽  
Exponential Growth Rate ◽  
Statistical Confidence ◽  
Italian Regions ◽  
The Impact

AbstractWe perform a statistical analysis for understanding the effect of the environmental temperature on the exponential growth rate of the cases infected by COVID-19 for US and Italian regions. In particular, we analyze the datasets of regional infected cases, derive the growth rates for regions characterized by readable exponential growth phase in their evolution spread curve and plot them against the environmental temperatures averaged within the same regions, derive the relationship between temperature and growth rate, and evaluate its statistical confidence. The results clearly support the first reported statistically significant relationship of negative correlation between the average environmental temperature and exponential growth rates of the infected cases. The critical temperature, which eliminates the exponential growth, and thus the COVID-19 spread in US regions, is estimated to be TC = 86.1 ± 4.3 F0.

scholarly journals A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dana Vaknin ◽  
Guy Amit ◽  
Amir Bashan
Keyword(s):  
Gene Regulation ◽  
Measurement Errors ◽  
Numerical Models ◽  
Expression Profiles ◽  
Small Sample ◽  
Biological Variability ◽  
Technical Variability ◽  
Gene Coordination ◽  
Cell To Cell Variability ◽  
Errors Analysis

AbstractRecent technological advances, such as single-cell RNA sequencing (scRNA-seq), allow the measurement of gene expression profiles of individual cells. These expression profiles typically exhibit substantial variations even across seemingly homogeneous populations of cells. Two main different sources contribute to this measured variability: actual differences between the biological activity of the cells and technical measurement errors. Analysis of the biological variability may provide information about the underlying gene regulation of the cells, yet distinguishing it from the technical variability is a challenge. Here, we apply a recently developed computational method for measuring the global gene coordination level (GCL) to systematically study the cell-to-cell variability in numerical models of gene regulation. We simulate ‘biological variability’ by introducing heterogeneity in the underlying regulatory dynamic of different cells, while ‘technical variability’ is represented by stochastic measurement noise. We show that the GCL decreases for cohorts of cells with increased ‘biological variability’ only when it is originated from the interactions between the genes. Moreover, we find that the GCL can evaluate and compare—for cohorts with the same cell-to-cell variability—the ratio between the introduced biological and technical variability. Finally, we show that the GCL is robust against spurious correlations that originate from a small sample size or from the compositionality of the data. The presented methodology can be useful for future analysis of high-dimensional ecological and biochemical dynamics.

scholarly journals High Throughput Centrifugal Electrospinning of Polyacrylonitrile Nanofibers for Carbon Fiber Nonwovens

Polymers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1313
Author(s):  
Andreas Hoffmann ◽  
Alexander J. C. Kuehne
Keyword(s):  
High Throughput ◽  
Carbon Nanofiber ◽  
Fiber Diameter ◽  
Carbon Precursor ◽  
Production Methods ◽  
High Throughput Method ◽  
Radial Forces ◽  
Fiber Deposition ◽  
The Impact ◽  
Pan Fibers

Carbon nanofiber nonwovens are promising materials for electrode or filtration applications; however, their utilization is obviated by a lack of high throughput production methods. In this study, we utilize a highly effective high-throughput method for the fabrication of polyacrylonitrile (PAN) nanofibers as a nonwoven on a dedicated substrate. The method employs rotational-, air pressure- and electrostatic forces to produce fibers from the inner edge of a rotating bell towards a flat collector. We investigate the impact of all above-mentioned forces on the fiber diameter, morphology, and bundling of the carbon-precursor PAN fibers. The interplay of radial forces with collector-facing forces has an influence on the uniformity of fiber deposition. Finally, the obtained PAN nanofibers are converted to carbon nonwovens by thermal treatment.

scholarly journals Changing trends and emissions of hydrochlorofluorocarbons and their hydrofluorocarbon replacements

2016 ◽  
Author(s):  
Peter G. Simmonds ◽  
Matthew Rigby ◽  
Archibold McCulloch ◽  
Simon O'Doherty ◽  
Dickon Young ◽  
...  
Keyword(s):  
Growth Rates ◽  
Montreal Protocol ◽  
Hfc 134A ◽  
Production And Consumption ◽  
Ozone Depleting Substances ◽  
Phase Out ◽  
Article 5 ◽  
The Impact ◽  
Global Emissions

Abstract. High frequency, in situ global observations of HCFC-22 (CHClF2), HCFC-141b (CH3CCl2F), HCFC-142b (CH3CClF2) and HCFC-124 (CHClFCF3) and their main HFC replacements HFC-134a (CH2FCF3), HFC-125 (CHF2CF3), HFC-143a (CH3CF3), and HFC-32 (CH2F2) have been used to determine their changing global growth rates and emissions in response to the Montreal Protocol and its recent amendments. The 2007 adjustment to the Montreal Protocol required the accelerated phase-out of HCFCs with global production and consumption capped in 2013, to mitigate their environmental impact as both ozone depleting substances and important greenhouse gases. We find that this change has coincided with a reduction in global emissions of the four HCFCs with aggregated global emissions in 2015 of 444 ± 75 Gg/yr, in CO2 equivalent units (CO2 e) 0.75 ± 0.1 Gt/yr, compared with 483 ± 70 Gg/yr (0.82 ± 0.1 Gt/yr CO2 e) in 2010. (All quoted uncertainties in this paper are 1 sigma). About 80 % of the total HCFC atmospheric burden in 2015 is HCFC-22, where global HCFC emissions appear to have been relatively constant in spite of the 2013 cap on global production and consumption. We attribute this to a probable increase in production and consumption of HCFC-22 in Montreal Protocol Article 5 (developing) countries and the continuing release of HCFC-22 from the large banks which dominate HCFC global emissions. Conversely, the four HFCs all show increasing annual growth rates with aggregated global HFCs emissions in 2015 of 329 ± 70 Gg/yr (0.65 ± 0.12 Gt/yr CO2 e) compared to 2010 with 240 ± 50 Gg/yr (0.47 ± 0.08 Gt/yr CO2 e). As HCFCs are replaced by HFCs we investigate the impact of the shift to refrigerant blends which have lower global warming potentials (GWPs). We also note that emissions of HFC-125 and HFC-32 appear to have increased more rapidly during the 2011–2015 5-yr period compared to 2006–2010.

scholarly journals High-throughput quantum-mechanics/molecular-mechanics (ONIOM) macromolecular crystallographic refinement withPHENIX/DivCon: the impact of mixed Hamiltonian methods on ligand and protein structure

2018 ◽  
Vol 74 (11) ◽  
pp. 1063-1077 ◽  
Author(s):  
Oleg Borbulevych ◽  
Roger I. Martin ◽  
Lance M. Westerhoff
Keyword(s):  
Quantum Mechanics ◽  
Molecular Mechanics ◽  
High Throughput ◽  
Active Site ◽  
Binding Modes ◽  
Acta Cryst ◽  
Structure Based Drug Design ◽  
Energy Functionals ◽  
Conventional Methods ◽  
The Impact

Conventional macromolecular crystallographic refinement relies on often dubious stereochemical restraints, the preparation of which often requires human validation for unusual species, and on rudimentary energy functionals that are devoid of nonbonding effects owing to electrostatics, polarization, charge transfer or even hydrogen bonding. While this approach has served the crystallographic community for decades, as structure-based drug design/discovery (SBDD) has grown in prominence it has become clear that these conventional methods are less rigorous than they need to be in order to produce properly predictive protein–ligand models, and that the human intervention that is required to successfully treat ligands and other unusual chemistries found in SBDD often precludes high-throughput, automated refinement. Recently, plugins to thePython-based Hierarchical ENvironment for Integrated Xtallography(PHENIX) crystallographic platform have been developed to augment conventional methods with thein situuse of quantum mechanics (QM) applied to ligand(s) along with the surrounding active site(s) at each step of refinement [Borbulevychet al.(2014),Acta CrystD70, 1233–1247]. This method (Region-QM) significantly increases the accuracy of the X-ray refinement process, and this approach is now used, coupled with experimental density, to accurately determine protonation states, binding modes, ring-flip states, water positions and so on. In the present work, this approach is expanded to include a more rigorous treatment of the entire structure, including the ligand(s), the associated active site(s) and the entire protein, using a fully automated, mixed quantum-mechanics/molecular-mechanics (QM/MM) Hamiltonian recently implemented in theDivConpackage. This approach was validated through the automatic treatment of a population of 80 protein–ligand structures chosen from the Astex Diverse Set. Across the entire population, this method results in an average 3.5-fold reduction in ligand strain and a 4.5-fold improvement inMolProbityclashscore, as well as improvements in Ramachandran and rotamer outlier analyses. Overall, these results demonstrate that the use of a structure-wide QM/MM Hamiltonian exhibits improvements in the local structural chemistry of the ligand similar to Region-QM refinement but with significant improvements in the overall structure beyond the active site.

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