Prediction of photoperiodic regulators from quantitative gene circuit models

Photoperiod sensors allow physiological adaptation to the changing seasons. The external coincidence hypothesis postulates that a light-responsive regulator is modulated by a circadian rhythm. Sufficient data are available to test this quantitatively in plants, though not yet in animals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their lightsensitive proteins are thought to form an external coincidence sensor. We use 40 timeseries of molecular data to model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, the models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modelling makes this complexity explicit and may thus contribute to crop improvement

Statistical bootstrapping methods in VaR calculation

Monte Carlo methods are often applied to problems in finance especially in the area of risk calculation by the Value-atRisk (VaR) measure. Different applications of statistical resampling techniques are shown, specifically bootstrapping, to refine the computational results in different ways. Methods are provided for improving backtesting stability, acceleration of Monte Carlo VaR convergence by orders of magnitude, and incorporating covariance matrix uncertainty in VaR figures. Existing methods are applied and new solutions developed. Extensive numerical tests on large numbers of randomly generated portfolios prove the effectiveness of the suggested solutions.Value-AT-RISK, Monte Carlo, Resampling, Variance Reduction, Finance,