85 research outputs found
Profiles
Biographical sketches of Harry Clark Bentley by Edward James Gurry; Joseph E. Sterrrett by Barbara Dubis Marino; Perry Mason by Roscoe Eugene Bryson, Jr.; Robert Hiester Montgomery by Anthony T. Krzystofik
Chemotactic response and adaptation dynamics in Escherichia coli
Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia
coli is integral for detecting chemicals over a wide range of background
concentrations, ultimately allowing cells to swim towards sources of attractant
and away from repellents. Its biochemical mechanism based on methylation and
demethylation of chemoreceptors has long been known. Despite the importance of
adaptation for cell memory and behavior, the dynamics of adaptation are
difficult to reconcile with current models of precise adaptation. Here, we
follow time courses of signaling in response to concentration step changes of
attractant using in vivo fluorescence resonance energy transfer measurements.
Specifically, we use a condensed representation of adaptation time courses for
efficient evaluation of different adaptation models. To quantitatively explain
the data, we finally develop a dynamic model for signaling and adaptation based
on the attractant flow in the experiment, signaling by cooperative receptor
complexes, and multiple layers of feedback regulation for adaptation. We
experimentally confirm the predicted effects of changing the enzyme-expression
level and bypassing the negative feedback for demethylation. Our data analysis
suggests significant imprecision in adaptation for large additions.
Furthermore, our model predicts highly regulated, ultrafast adaptation in
response to removal of attractant, which may be useful for fast reorientation
of the cell and noise reduction in adaptation.Comment: accepted for publication in PLoS Computational Biology; manuscript
(19 pages, 5 figures) and supplementary information; added additional
clarification on alternative adaptation models in supplementary informatio
A Density-Dependent Switch Drives Stochastic Clustering and Polarization of Signaling Molecules
Positive feedback plays a key role in the ability of signaling molecules to form highly localized clusters in the membrane or cytosol of cells. Such clustering can occur in the absence of localizing mechanisms such as pre-existing spatial cues, diffusional barriers, or molecular cross-linking. What prevents positive feedback from amplifying inevitable biological noise when an un-clustered “off” state is desired? And, what limits the spread of clusters when an “on” state is desired? Here, we show that a minimal positive feedback circuit provides the general principle for both suppressing and amplifying noise: below a critical density of signaling molecules, clustering switches off; above this threshold, highly localized clusters are recurrently generated. Clustering occurs only in the stochastic regime, suggesting that finite sizes of molecular populations cannot be ignored in signal transduction networks. The emergence of a dominant cluster for finite numbers of molecules is partly a phenomenon of random sampling, analogous to the fixation or loss of neutral mutations in finite populations. We refer to our model as the “neutral drift polarity model.” Regulating the density of signaling molecules provides a simple mechanism for a positive feedback circuit to robustly switch between clustered and un-clustered states. The intrinsic ability of positive feedback both to create and suppress clustering is a general mechanism that could operate within diverse biological networks to create dynamic spatial organization
Global disparities in SARS-CoV-2 genomic surveillance
Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity
Global disparities in SARS-CoV-2 genomic surveillance
Genomic sequencing is essential to track the evolution and spread of SARS-CoV-2, optimize molecular tests, treatments, vaccines, and guide public health responses. To investigate the global SARS-CoV-2 genomic surveillance, we used sequences shared via GISAID to estimate the impact of sequencing intensity and turnaround times on variant detection in 189 countries. In the first two years of the pandemic, 78% of high-income countries sequenced >0.5% of their COVID-19 cases, while 42% of low- and middle-income countries reached that mark. Around 25% of the genomes from high income countries were submitted within 21 days, a pattern observed in 5% of the genomes from low- and middle-income countries. We found that sequencing around 0.5% of the cases, with a turnaround time <21 days, could provide a benchmark for SARS-CoV-2 genomic surveillance. Socioeconomic inequalities undermine the global pandemic preparedness, and efforts must be made to support low- and middle-income countries improve their local sequencing capacity
Melting and Solidifying Behaviors of the CaO–SiO2–FeOx Slags at Various Oxygen Partial Pressures
Fecal Microbiota Transplant From a Rational Stool Donor Improves Hepatic Encephalopathy: A Randomized Clinical Trial REPLY
Graphical representation of oxygen and carbon potentials in equilibrium with impure high-temperature inert gases
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