Water Cherenkov Detectors response to a Gamma Ray Burst in the Large Aperture GRB Observatory

In order to characterise the behaviour of Water Cherenkov Detectors (WCD) under a sudden increase of 1 GeV - 1 TeV background photons from a Gamma Ray Burst (GRB), simulations were conducted and compared to data acquired by the WCD of the Large Aperture GRB Observatory (LAGO). The LAGO operates arrays of WCD at high altitude to detect GRBs using the single particle technique. The LAGO sensitivity to GRBs is derived from the reported simulations of the gamma initiated particle showers in the atmosphere and the WCD response to secondaries.Comment: 5 pages, proceeding of the 31st ICRC 200

The Large Aperture GRB Observatory

The Large Aperture GRB Observatory (LAGO) is aiming at the detection of the high energy (around 100 GeV) component of Gamma Ray Bursts, using the single particle technique in arrays of Water Cherenkov Detectors (WCD) in high mountain sites (Chacaltaya, Bolivia, 5300 m a.s.l., Pico Espejo, Venezuela, 4750 m a.s.l., Sierra Negra, Mexico, 4650 m a.s.l). WCD at high altitude offer a unique possibility of detecting low gamma fluxes in the 10 GeV - 1 TeV range. The status of the Observatory and data collected from 2007 to date will be presented.Comment: 4 pages, proceeding of 31st ICRC 200

Modeling Stochasticity and Variability in Gene Regulatory Networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This paper contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.Comment: 23 pages, 8 figure

Control Strategy Identification via Trap Spaces in Boolean Networks

The control of biological systems presents interesting applications such as cell reprogramming or drug target identification. A common type of control strategy consists in a set of interventions that, by fixing the values of some variables, force the system to evolve to a desired state. This work presents a new approach for finding control strategies in biological systems modeled by Boolean networks. In this context, we explore the properties of trap spaces, subspaces of the state space which the dynamics cannot leave. Trap spaces for biological networks can often be efficiently computed, and provide useful approximations of attraction basins. Our approach provides control strategies for a target phenotype that are based on interventions that allow the control to be eventually released. Moreover, our method can incorporate information about the attractors to find new control strategies that would escape usual percolation-based methods. We show the applicability of our approach to two cell fate decision models.Comment: 16 pages, 2 figure

Wealth from Health: Linking Social Investments to Earnings in Latin America

Common sense suggests that healthier people are more productive and wealthier people can obtain things that make them healthier. This book asks whether investments in health also affect productivity and how public policy can influence this relationship. These questions are probed through a series of Latin American case studies, using household survey data on individuals to analyze the relationships between efforts to improve health on the one hand, and the potential impact of health status on individual hourly earnings on the other. By analyzing these relationships together- health determinants and the impact of health on earnings- it becomes possible to assess the effectiveness of particular strategies for improving health status and to see the critical importance of health as a component of "human capital"