Time-varying Sensitivity Research Articles

Using dynamic sensitivities to characterize metabolic reaction systems

Metabolite concentrations in cells are governed by enzyme kinetics in the metabolic reaction system. One can analyze how these concentrations depend on system variables such as enzyme activities by computing system sensitivities, which generally vary with time. Dynamic sensitivities, i.e., time-varying sensitivities, reflect the time-dependent response of the metabolic reaction network to perturbations. Unfortunately, dynamic sensitivity profiles are not commonly used in the analysis of metabolic reaction systems. In the present study, we demonstrate the use of dynamic logarithmic gains, i.e., normalized time-varying sensitivities, to gain insights into the dynamic behavior of metabolic networks. A biosynthetic reaction model of aromatic amino acids proposed by other researchers is used as a case study. The model system is analyzed using the dynamic logarithmic gains in parallel with simulations of the time-transient behavior of metabolite concentrations and metabolic fluxes. The result indicates that the influences of independent variables are most pronounced just after perturbations and the effects of perturbations on metabolite concentration at early times can be larger than those at steady state. These findings suggest that it is important to perform dynamic sensitivity analysis in addition to steady-state analysis. Furthermore, the rankings of the bottleneck ranking indicators, defined as the product of dynamic logarithmic gain and metabolite concentration, for three desired amino acids reveal that the degree of bottleneck for each enzyme changes with time. In conclusion, the dynamic logarithmic gains are not only useful for analyzing metabolic reaction systems but also can offer additional insights on the transient behavior of the system over steady state sensitivities, leading to a proper design of metabolic systems.

Circadian variability of insulin sensitivity: physiological input for in silico artificial pancreas.

Closed-loop control clinical research trials have been considerably accelerated by in silico trials using the Food and Drug Administration-accepted type 1 diabetes mellitus (T1DM) simulator. We have recently demonstrated that postprandial insulin sensitivity (SI) in T1DM subjects was lower at breakfast (B) than lunch (L) and dinner (D), but not significantly, because of the small population size. The goal of this study was therefore to incorporate this novel information into the University of Virginia/Padova T1DM simulator and to reproduce in silico the observed circadian variability. Twenty T1DM subjects received an identical mixed meal at B, L, and D. SI was calculated for each meal using the oral glucose minimal model. Seven SI daily patterns were identified, and their probabilities were estimated. Each in silico subject was linked to a time-varying SI profile, while random deviations of up to 40% were allowed. Simulations were compared with experimental data. The integrated area above the basal glucose curve values were 2.60 ± 0.91 (B), 1.38 ± 0.91 (L), and 1.44 ± 1.07 (D) 10(4) min · mg/dL in silico versus 2.87 ± 1.65 (B), 1.98 ± 1.56 (L), and 2.16 ± 2.00 (D) 10(4) min · mg/dL in vivo. Incremental peak glucose values were 109 ± 33 (B), 80 ± 29 (L), and 81 ± 30 (D) mg/dL in silico versus 136 ± 39 (B), 126 ± 37 (L), and 125 ± 48 (D) mg/dL in vivo. The incorporation of a time-varying SI into the simulator makes this technology suitable for running multiple-meal scenarios, thus enabling a more robust design of artificial pancreas algorithms.

Precautionary Volatility and Asset Prices

Many theories of asset prices assume time-varying uncertainty in order to generate time-varying risk premia. This paper generates time-varying uncertainty endogenously, through precautionary saving dynamics. Precautionary motives prescribe that, in bad times, next period's consumption should be very sensitive to news. This time-varying sensitivity results in time-varying consumption volatility. Production makes this channel visible, and external habit preferences amplify it. An estimated model featuring this channel quantitatively accounts for excess return and dividend predictability regressions. It also matches the first two moments of excess equity returns, the risk-free rate, and the second moments of consumption, output, and investment.

Precautionary Volatility and Asset Prices

Many theories of asset prices assume time-varying uncertainty in order to generate time-varying risk premia. This paper generates time-varying uncertainty endogenously, through precautionary saving dynamics. Precautionary motives prescribe that, in bad times, next period's consumption should be very sensitive to news. This time-varying sensitivity results in time-varying consumption volatility. Production makes this channel visible, and external habit preferences amplify it. An estimated model featuring this channel quantitatively accounts for excess return and dividend predictability regressions. It also matches the first two moments of excess equity returns, the risk-free rate, and the second moments of consumption, output, and investment.

The Time-varying Cash Flow Sensitivity of Cash

By using data of US manufacturing companies, we revisit the cash flow sensitivity to cash in two sub-samples of 1993-2000 and 2000-2011 to investigate the time-varying features of the cash flow sensitivity of cash. Our results show a weakening coefficient of US manufacturing firms from 1990s to 2000s. The sensitivity in the later time period is only a half of its original scale. Financially unconstrained firms seem to converge with the constrained firms in the later period, leading to the conclusion that macroeconomic conditions impact more on the cash flow sensitivity of cash than the external financial constraint does. Further, our research identifies that the overall decreasing sensitivity is driven by firms with negative cash flows.

Analysing the Sub-processes of a Conceptual Rainfall-Runoff Model Using Information About the Parameter Sensitivity and Variance

Sensitivity analyses are often carried out for the main model output and using a predefined evaluation period. It is possible, however, to get much more knowledge about how the system works when estimating the sensitivity for the output of individual modules for each time step (time-varying sensitivity analysis). This is shown here using a variance-based sensitivity analysis for a conceptual rainfall-runoff model applied to a mountainous catchment. The first-order and total sensitivities were computed using Sobol’s method. Since the parameter ranges used in the sensitivity analysis were obtained through a Markov chain Monte Carlo (MCMC) sampling, the sensitivity indices reflect the parameter uncertainty and make a good use of the previous available information. As a first step, the variance of each flow component was calculated. The flow component with the highest variance at each time step can be regarded as the ‘dominant physical control’, which has been defined as the parameter to which the model output reacts in a highly sensitive way when the parameter varies within realistic ranges. This information about the dominant processes can be used for facilitating model calibration by identifying the periods on which to focus when calibrating different parameters. It also can be useful for estimating the amount of data available for calibrating each process. The second part presents the total sensitivity indices and interactions for individual flow components considering a 2-year period. The results show large differences in the time-varying sensitivity patterns of the flow components. It is concluded that such a high-resolution sensitivity analysis for each flow component is a good complement to a sensitivity analysis of the total discharge, increasing our understanding about the internal functioning of individual modules which can be helpful when comparing different model formulations.

Optimal Asset Pricing

We describe an R package for determining the optimal price of an asset which is “perishable” in a certain sense, given the intensity of customer arrivals and a time-varying price sensitivity function which specifies the probability that a customer will purchase an asset offered at a given price at a given time. The package deals with the case of customers arriving in groups, with a probability distribution for the group size being specified. The methodology and software allow for both discrete and continuous pricing. The class of possible models for price sensitivity functions is very wide, and includes piecewise linear models. A mechanism for constructing piecewise linear price sensitivity functions is provided.

SYSTEMATIC RISK DURING 2008–2009 RECESSION IN EMERGING MARKETS: SOME EVIDENCE FROM V3 AND BALTIC ECONOMIES

Abrupt and profound swings in economic activity can result in changes in systematic component of risk premia of capital market assets. This can translate into adjustments in risk perception by the market agents, which may lead to significant changes in real investment development. We examine the issue of time-varying systematic risk on a micro level using the capital asset pricing model in an intertemporal setting. We formulate the hypothesis within a bivariate GARCH-in-mean model, which enables us to estimate the time-varying variances and covariances of the respective assets and market returns and thus the time-varying sensitivity to systematic risk. The results of the paper show that the reaction of assets’ sensitivity to systematic risk varies across the sample and the changes were rather temporary. Based on the results, the downturn in economic activity witnessed in 2008 – 2009 should not be a drag on real investment.

From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models

Abstract. Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between sets of dominant parameters or processes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between dominant parameters and processes in the absence of observations, such as under nonstationarity.

A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales

Assessing the time-varying sensitivity of environmental models has become a common approach to understand both the value of different data periods for estimating specific parameters, and as part of a diagnostic analysis of the model structure itself (i.e. whether dominant processes are emerging in the model at the right times and over the appropriate time periods). It is not straightforward to visualize these results though, given that the window size over which the time-varying sensitivity is best integrated generally varies for different parameters. In this short communication we present a new approach to visualizing such time-varying sensitivity across time scales of integration. As a case study, we estimate first order sensitivity indices with the FAST (Fourier Amplitude Sensitivity Test) method for a typical conceptual rainfall–runoff model. The resulting plots can guide data selection for model calibration, support diagnostic model evaluation and help to define the timing and length of spot gauging campaigns in places where long-term calibration data are not yet available.

Caloric Restriction Alters the Metabolic Response to a Mixed-Meal: Results from a Randomized, Controlled Trial

ObjectivesTo determine if caloric restriction (CR) would cause changes in plasma metabolic intermediates in response to a mixed meal, suggestive of changes in the capacity to adapt fuel oxidation to fuel availability or metabolic flexibility, and to determine how any such changes relate to insulin sensitivity (SI).MethodsForty-six volunteers were randomized to a weight maintenance diet (Control), 25% CR, or 12.5% CR plus 12.5% energy deficit from structured aerobic exercise (CR+EX), or a liquid calorie diet (890 kcal/d until 15% reduction in body weight)for six months. Fasting and postprandial plasma samples were obtained at baseline, three, and six months. A targeted mass spectrometry-based platform was used to measure concentrations of individual free fatty acids (FFA), amino acids (AA), and acylcarnitines (AC). SI was measured with an intravenous glucose tolerance test.ResultsOver three and six months, there were significantly larger differences in fasting-to-postprandial (FPP) concentrations of medium and long chain AC (byproducts of FA oxidation) in the CR relative to Control and a tendency for the same in CR+EX (CR-3 month P = 0.02; CR-6 month P = 0.002; CR+EX-3 month P = 0.09; CR+EX-6 month P = 0.08). After three months of CR, there was a trend towards a larger difference in FPP FFA concentrations (P = 0.07; CR-3 month P = 0.08). Time-varying differences in FPP concentrations of AC and AA were independently related to time-varying SI (P<0.05 for both).ConclusionsBased on changes in intermediates of FA oxidation following a food challenge, CR imparted improvements in metabolic flexibility that correlated with improvements in SI.Trial RegistrationClinicalTrials.gov NCT00099151

Time-Varying Mutual Fund Performance-Flow Sensitivity and Managerial Effort

It is well-documented that mutual fund flows are positively related to funds’ past performance. This paper focuses on the time-series variation of the performance-flow relationship. I find that investors are more sensitive to fund performance in some periods than other periods and that the sensitivity difference is statistically and economically significant. The variation in sensitivity could be driven by many factors, including the signaling value of fund performance, investor attention, and wealth shocks. Furthermore, a large part of change in sensitivity is predictable based on public information. The variation in sensitivity could affect managers’ incentives: the payoff for managerial effort is high in a high-sensitivity period. The panel regression results show that managers will work harder when expected sensitivity is high, which is reflected by a larger deviation from benchmark index holdings, more trading, and more information collection. The results are robust to alternative explanations and out-of-sample test.

Introduction

Introduction

Fiscal Policy and Interest Rates: The Role of Sovereign Default Risk

Recent events have highlighted the potential importance of nonlinear efiects of flscal variables (notably debt and deflcits) on interest rates: While in times when government solvency is not a concern the standard crowding-out efiects are of moderate magnitude, in times when default risk becomes an issue the interest rate efiects can become very large. This paper provides new evidence on the magnitude of these efiects. For the case when default risk is not a concern, it uses an arbitrage-free term structure model to estimate the dynamic efiects of flscal policy shocks on interest rates along the entire maturity spectrum. For the case when default risk becomes a concern (thereby violating a central assumption of the term structure model), I present evidence based on EMU government bond spread regressions on time-varying efiects of national flscal policies on spreads as well as the time-varying sensitivity of yield spreads to international risk aversion as a function of the state of flscal policy. JEL classiflcation: E6, H6.

Time-Varying Mutual Fund Performance-Flow Sensitivity and Managerial Effort

Time-Varying Mutual Fund Performance-Flow Sensitivity and Managerial Effort

A Time-Variant Analysis of Phase Noise in Series Quadrature Oscillators

This paper presents a systemic analysis for phase noise performances of the series quadrature oscillator (QOSC) by using the time-variant impulse sensitivity function (ISF) model. The effective ISF for each noise source in the oscillator is derived mathematically. According to these effective ISFs, the explicit closed-form expression for phase noise due to the total thermal noise in the series QOSC is derived, and the phase noise contribution from the flicker noise in the regenerative and coupling transistors is also figured out. The phase noise contributions from the thermal noise and the flicker noise are verified by SpectreRF simulations.

Quantitative estimates of velocity sensitivity to surface melt variations at a large Greenland outlet glacier

AbstractThe flow speed of Greenland outlet glaciers is governed by several factors, the relative importance of which is poorly understood. The delivery of surface-generated meltwater to the bed of alpine glaciers has been shown to influence glacier flow speed when the volume of water is sufficient to increase basal fluid pressure and hence basal lubrication. While this effect has also been demonstrated on the Greenland ice-sheet margin, little is known about the influence of surface melting on the large, marine-terminating outlet glaciers that drain the ice sheet. We use a validated model of meltwater input and GPS-derived surface velocities to quantify the sensitivity of glacier flow speed to changes in surface melt at Helheim Glacier during two summer seasons (2007–08). Our observations span ∼55 days near the middle of each melt season. We find that relative changes in glacier speed due to meltwater input are small, with variations of ∼45% in melt producing changes in velocity of ∼2–4%. These velocity variations are, however, of similar absolute magnitude to those observed at smaller glaciers and on the ice-sheet margin. We find that the glacier’s sensitivity to variations in meltwater input decreases approximately exponentially with distance from the calving front. Sensitivity to melt varies with time, but generally increases as the melt season progresses. We interpret the time-varying sensitivity of glacier flow to meltwater input as resulting from changes in subglacial hydraulic routing caused by the changing volume of meltwater input.

Bayesian Estimation of the Time‐Varying Sensitivity of a Diagnostic Test with Application to Mother‐to‐Child Transmission of HIV

We present a Bayesian model to estimate the time-varying sensitivity of a diagnostic assay when the assay is given repeatedly over time, disease status is changing, and the gold standard is only partially observed. The model relies on parametric assumptions for the distribution of the latent time of disease onset and the time-varying sensitivity. Additionally, we illustrate the incorporation of historical data for constructing prior distributions. We apply the new methods to data collected in a study of mother-to-child transmission of HIV and include a covariate for sensitivity to assess whether two different assays have different sensitivity profiles.

Time-varying real estate sensitivities of mortgage REITs

This study examines the linkage between mortgage Real Estate Investment Trust (REIT) returns and the private real estate factor. The results show that real estate sensitivities of mortgage REITs are time varying and have been significant since 2000. Furthermore, home-financing mortgage REITs appear to be more sensitive to real estate market fluctuations than commercial-financing mortgage REITs are. This differential sensitivity is consistent with the notion that the default risk and the prepayment risk associated with residential mortgage loans are more closely related to real estate market conditions than are commercial mortgage loans.

Identification of bottleneck enzymes with negative dynamic sensitivities: Ethanol fermentation systems as case studies

Dynamic logarithmic gains (normalised time-varying sensitivities) or their products with metabolite concentrations, called an instantaneous bottleneck ranking indicator (instantaneous BR indicator), can express the time courses of bottleneck enzyme ranking. The integrated value of the instantaneous BR indicator, called an overall BR indicator, provides a ranking of bottleneck enzymes over the entire reaction period of time. Owing to the property of the logarithmic gains, these BR indicators are allowed to include both positive and negative values and in turn raise several questions on the identification of a bottleneck enzyme. To eliminate these questions, the present work studies the identification of bottleneck enzymes in four ethanol fermentation systems using suspended and immobilised cells at pH 4.5 and 5.5 as case studies. These systems consist of both intracellular reactions without cell growth (intracellular reaction systems) and those with cell growth (cell growth systems). The analyses of the intracellular reaction systems reveal that both the suspended and immobilised cell systems at pH 5.5 are not robust. In the cell growth systems, therefore, the analyses are conducted only for the suspended and immobilised cell models at pH 4.5. The enzyme glyceraldehyde 3-phosphate dehydrogenase (gapd) initially has the largest logarithmic gains under all conditions, whereas the system becomes insensitive to it over time. Interestingly, the instantaneous BR indicator for gapd is negatively large. Also, other indicators change in positive and/or negative regions. To deal with these values of different signs, all the indicators are integrated to obtain and rank the overall BR indicators in the order of the magnitudes of their absolute values. The result shows that the ranking of enzymes are completely identical to that of the final ethanol concentrations obtained by finitely increasing or decreasing the relevant enzyme activities in accordance with the signs of overall BR indicators. This result indicates that the enzymes must be ranked in the order of the magnitudes of their absolute values to determine a bottleneck enzyme. As a result, the analyses of the intracellular reaction systems reveal that the bottleneck enzyme is switched from a membrane-bound glucose transporter enzyme to phosphofructokinase when the cell-retaining condition is changed from suspension to immobilisation. In the cell growth systems, gapd is identified as the most likely bottleneck enzyme, suggesting that the gapd activity must be decreased to increase the ethanol productivity.