26 research outputs found

    Revealing the nature of radar reflections in ice: DEP-based FDTD forward modeling

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    Successful simulation of ground penetrating radar (GPR) traces in polar ice is achieved bynumerical finite-difference time-domain (FDTD) forward modeling.Properties of the modeled medium are taken from high resolution dielectric profiling (DEP)of the upper 100~m of an ice core from Dronning Maud Land, Antarctica.The GPR reference trace is calculated from stacking of a normal moveout correctedcommon-midpoint survey, carried out near the borehole location.The excellent agreement of synthetic and GPR-based results demonstrates the capability ofFDTD models to reproduce radargrams from ice core properties forinterpretation of radio echo sounding data, and emphasizes the exploitation of radar datafor improved interpretations of glaciological climate proxys.In addition to the presentation of modeling results, we perform sensitivity experiments toinvestigate the nature and origin of radar reflection in ice,discuss reasons for the failure of modeling studies in the past, and indicate new approaches

    How quantitative measures unravel design principles in multi-stage phosphorylation cascades.

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    We investigate design principles of linear multi-stage phosphorylation cascades by using quantitative measures for signaling time, signal duration and signal amplitude. We compare alternative pathway structures by varying the number of phosphorylations and the length of the cascade. We show that a model for a weakly activated pathway does not reflect the biological context well, unless it is restricted to certain parameter combinations. Focusing therefore on a more general model, we compare alternative structures with respect to a multivariate optimization criterion. We test the hypothesis that the structure of a linear multi-stage phosphorylation cascade is the result of an optimization process aiming for a fast response, defined by the minimum of the product of signaling time and signal duration. It is then shown that certain pathway structures minimize this criterion. Several popular models of MAPK cascades form the basis of our study. These models represent different levels of approximation, which we compare and discuss with respect to the quantitative measures

    How Does a Single Cell Know When the Liver Has Reached Its Correct Size?

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    <div><p>The liver is a multi-functional organ that regulates major physiological processes and that possesses a remarkable regeneration capacity. After loss of functional liver mass the liver grows back to its original, individual size through hepatocyte proliferation and apoptosis. How does a single hepatocyte ‘know’ when the organ has grown to its final size? This work considers the initial growth phase of liver regeneration after partial hepatectomy in which the mass is restored. There are strong and valid arguments that the trigger of proliferation after partial hepatectomy is mediated through the portal blood flow. It remains unclear, if either or both the concentration of metabolites in the blood or the shear stress are crucial to hepatocyte proliferation and liver size control. A cell-based mathematical model is developed that helps discriminate the effects of these two potential triggers. Analysis of the mathematical model shows that a metabolic load and a hemodynamical hypothesis imply different feedback mechanisms at the cellular scale. The predictions of the developed mathematical model are compared to experimental data in rats. The assumption that hepatocytes are able to buffer the metabolic load leads to a robustness against short-term fluctuations of the trigger which can not be achieved with a purely hemodynamical trigger.</p></div

    Simulation results of the HD model.

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    <p><b>A</b> Temporary progression of the lobule size in dependence of , which is the sensitivity of the proliferation response to deviations from the normal shear stress value . <b>B</b> Temporary progression of the lobule size in the simulation with (red curve in <b>A</b>) in the scenarios (pHx), (STP) and (PVL) as given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093207#pone-0093207-t001" target="_blank">Table 1</a>. The shear stress strength is illustrated by a color-code for the different scenarios. The time point at which compensatory growth is 90% completed is marked with . <b>C</b> Spatial distribution of proliferation events for the scenario (pHx). For each time point, the hepatocyte layer is equally divided into three sections displayed on the horizontal axis. The proliferation events are recorded for each section over the entire simulation time and are depicted as number of proliferation events for different relative spatial positions. <b>D</b> The frequency of proliferation events for scenario (pHx) is depicted as number of proliferation events per time point.</p

    Feedback mechanisms of liver size regulation after partial hepatectomy.

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    <p>(A) Sketch of hypothesized mechanisms of liver size regulation after partial hepatectomy. Liver size regulation is modulated by hepatocyte (HC) proliferation and apoptosis. A partial hepatectomy (pHx) alters liver size and therefore the relation between the organ and the organism. As a consequence, changes in the portal blood flow are observed. HCs respond to portal blood flow changes. The two hypothesized triggers for proliferation or apoptosis that relate to the portal blood flow are the altered metabolic load (ML) per HC and the altered hemodynamics (HD). (B) Feedback mechanism for the HD hypothesis. The main assumption is marked in green. The partial hepatectomy results in a change of the shear stress level which triggers proliferation and therefore affects the hepatocyte (HC) number . (C) Feedback mechanism for the ML hypothesis. The main assumption is marked in green. The partial hepatectomy alters the metabolic load per hepatocyte (HC) and hence the intracellular buffer level . The functional relation between the buffer rate and is illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093207#pone.0093207.s003" target="_blank">Figure S3B</a>. The intracellular buffer affects the HC number . The according growth rate per hepatocyte is given by , see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093207#pone.0093207.s003" target="_blank">Figure S3D</a>. At the same time, HCs degrade metabolites from the intracellular buffer at the rate which depends on , see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093207#pone.0093207.s003" target="_blank">Figure S3C</a>.</p
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