Natural selection dictates that cells constantly adapt to dynamically changing environments in a context-dependent manner. Gene-regulatory networks often mediate the cellular response to perturbation 1-3, and an understanding of cellular adaptation will require experimental approaches aimed at subjecting cells to a dynamic environment that mimics their natural habitat 4-9. Here, we monitor the response of S. cerevisiae metabolic gene regulation to periodic changes in the external carbon source by utilizing a microfluidic platform that allows precise, dynamic control over environmental conditions. We find that the metabolic system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fluctuations that are too fast for the cell to mount an efficient response. We use computational modeling calibrated with experimental data to determine how frequency selection in the system is controlled by the interaction of coupled regulatory networks governing the signal transduction of alternative carbon sources. Experimental verification of model predictions leads to the discovery of two novel properties of the regulatory network. First, we reveal a previously unknown mechanism for post-transcriptional control, by demonstrating that two key transcripts are degraded at a rate that depends on the carbon source. Second, we compare two S. cerevisiae strains and find that they exhibit the same frequency response despite having markedly different induction characteristics. Our results suggest that while certain characteristics of the complex networks may differ when probed in a static environment, the system has been optimized for a robust response to a dynamically changing environment. Importantly, the integration of a novel experimental platform with numerical simulations revealed previously masked network properties, and the approach establishes a framework for dynamically probing organisms in order to reveal mechanisms that have evolved to mediate cellular responses to unpredictable environments.
Cell differentiation requires the ability to detect and respond appropriately to a variety of extracellular signals. Here we investigate a differentiation switch induced by changes in the concentration of a single stimulus. Yeast cells exposed to high doses of mating pheromone undergo cell division arrest. Cells at intermediate doses become elongated and divide in the direction of a pheromone gradient (chemotropic growth). Either of the pheromone-responsive MAP kinases, Fus3 and Kss1, promotes cell elongation, but only Fus3 promotes chemotropic growth. Whereas Kss1 is activated rapidly and with a graded dose-response profile, Fus3 is activated slowly and exhibits a steeper dose-response relationship (ultrasensitivity). Fus3 activity requires the scaffold protein Ste5; when binding to Ste5 is abrogated, Fus3 behaves like Kss1, and the cells no longer respond to a gradient or mate efficiently with distant partners. We propose that scaffold proteins serve to modulate the temporal and dose-response behavior of the MAP kinase.
Since the past decade, rapid development in nanotechnology has produced several aspects for the scientists and technologists to look into. Nanofluid is one of the incredible outcomes of such advancement. Nanofluids (colloidal suspensions of metallic and nonmetallic nanoparticles in conventional base fluids) are best known for their remarkable change to enhanced heat transfer abilities. Earlier research work has already acutely focused on thermal conductivity of nanofluids. However, viscosity is another important property that needs the same attention due to its very crucial impact on heat transfer. Therefore, viscosity of nanofluids should be thoroughly investigated before use for practical heat transfer applications. In this contribution, a brief review on theoretical models is presented precisely. Furthermore, the effects of nanoparticles' shape and size, temperature, volume concentration, pH, etc. are organized together and reviewed.
The maintenance and detection of signaling gradients are critical for proper development and cell migration. In single-cell organisms, gradient detection allows cells to orient toward a distant mating partner or nutrient source. Budding yeast expand their growth toward mating pheromone gradients through a process known as chemotropic growth. MATα cells secrete α-factor pheromone that stimulates chemotropism and mating differentiation in MATa cells and vice versa. Paradoxically, MATa cells secrete Bar1, a protease that degrades α-factor and that attenuates the mating response, yet is also required for efficient mating. We observed that MATa cells avoid each other during chemotropic growth. To explore this behavior, we developed a computational platform to simulate chemotropic growth. Our simulations indicated that the release of Bar1 enabled individual MATa cells to act as α-factor sinks. The simulations suggested that the resultant local reshaping of pheromone concentration created gradients that were directed away from neighboring MATa cells (self-avoidance) and that were increasingly amplified toward partners of the opposite sex during elongation. The behavior of Bar1-deficient cells in gradient chambers and mating assays supported these predictions from the simulations. Thus, budding yeast dynamically remodel their environment to ensure productive responses to an external stimulus and avoid nonproductive cell-cell interactions.
We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across diverse age-groups and ethnicities. A two-step approach was employed wherein cancer-positive samples were first identified as a group. A second multi-class algorithm then helped to distinguish between the individual cancers of the group. The approach yielded high detection sensitivity and specificity, highlighting its utility for the development of multi-cancer detection tests especially for early-stage cancers.
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