A reaction-diffusion model predicts the intracellular length scale over which EGFR-initiated GAB1-SHP2 complexes persist

Activation of receptor tyrosine kinases (RTKs) leads to the assembly of multi-membered protein complexes connected by phosphotyrosine-SH2 domain linkages. However, these linkages are relatively weak and reversible, which allows complex disassembly to occur on a time scale that permits phosphatases to dephosphorylate complex members and thereby regulate complex persistence. Here, we generated a computational reaction-diffusion model to predict the length scale over which membrane-bound RTKs can regulate the maintenance of such protein complexes through the intermediary action of diffusible cytoplasmic kinases. Specifically, we show that the RTK EGFR can activate SRC family kinases (SFKs) to maintain the association of SHP2 with phosphorylated GAB1, which activates SHP2, throughout the entire cell volume. This finding is dependent on the ability of SFKs to be activated by EGFR at the plasma membrane and subsequently diffuse through the cytosol, as altering the model topology to permit only SFK activation at the plasma membrane reduces the length scale of GAB1-SHP2 association. Modifying the model topology to neglect GAB1 binding to cytosolic and EGFR-bound GRB2 had little effect on this length scale. Indeed, a model sensitivity analysis identified protein diffusion, SFK inactivation, and GAB1 dephosphorylation as the processes that most strongly control the distance over which GAB1-SHP2 persists distal from EGFR. A model scaling analysis likewise predicted that the length scale of GAB1-SHP2 association is greatly extended compared to that of SFK activation and that GAB1-SHP2 complexes persist throughout the cell volume. Furthermore, the same processes identified in the model sensitivity analysis appeared in the length scale estimate for GAB1-SHP2 association. In vitro experiments using proximity ligation assay and immunofluorescence against GAB1-SHP2 and EGFR, respectively, suggested that GAB1-SHP2 complexes are distributed throughout cells and exist distally from EGFR during EGF stimulation. Overall, our results suggest that GAB1-SHP2 complexes—and thus active SHP2—can persist distally from EGFR due to re-phosphorylation of GAB1 throughout the cytosol by EGFR-activated SFKs.

Representing Low-Intensity Fire Sensible Heat Output in a Mesoscale Atmospheric Model with a Canopy Submodel: A Case Study with ARPS-CANOPY (version 5.2.12)

Mesoscale models are a class of atmospheric numerical model designed to simulate atmospheric phenomena with horizontal scales of about 2–200 km, although they are also applied to microscale phenomena, with horizontal scales less than about 2 km. Mesoscale models are capable of simulating wildland fire impacts on atmospheric flows if combustion by-products (e.g., heat, smoke) are properly represented in the model. One of the primary challenges encountered in applying a mesoscale model to studies of fire-perturbed flows is the representation of the fire sensible heat source in the model. Two primary methods have been implemented previously: turbulent sensible heat flux, either in the form of an exponentially-decaying vertical heat flux profile or surface heat flux; and soil temperature perturbation. In this study, the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy submodel, is utilized to simulate the turbulent atmosphere during a low-intensity operational prescribed fire in the New Jersey Pine Barrens. The study takes place in two phases: model assessment and model sensitivity. In the model assessment phase, analysis is limited to a single control simulation in which the fire sensible heat source is represented as an exponentially-decaying vertical profile of turbulent sensible heat flux. In the model sensitivity phase, a series of simulations are conducted to explore the sensitivity of model-observation agreement to (i) the method used to represent the fire sensible heat source in the model and (ii) parameters controlling the magnitude and vertical distribution of the sensible heat source. In both phases, momentum and scalar fields are compared between the model simulations and data obtained from six flux towers located within and adjacent to the burn unit. The multi-dimensional model assessment confirms that the model reproduces the background and fire-perturbed atmosphere as depicted by the tower observations, although the model underestimates the turbulent kinetic energy at the top of the canopy at several towers. The model sensitivity tests reveal that the best agreement with observations occurs when the fire sensible heat source is represented as a turbulent sensible heat flux profile, with surface heat flux magnitude corresponding to the peak 1-min mean observed heat flux averaged across the flux towers, and an e-folding extinction depth corresponding to the average canopy height in the burn unit. The study findings provide useful guidance for improving the representation of the sensible heat released from low-intensity prescribed fires in mesoscale models.

Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: a Case Study for the Contiguous USA

Surface meteorological analyses are an essential input (termed ‘forcing’) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily-average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin Hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

Analysis of hydrological processes with semi-distributed model

The Soil and Water Assessment Tool (SWAT), a semi-distributed physically-based hydrological model, is broadly used for simulating streamflow and analyzing hydrological processes in the basin. The SWAT model was applied to analyze the hydrological processes in Göksu Himmetli, Zamanti-Ergenuşağı, Göksun Poskoflu ve Hurman-Gözler Üstü sub-basins in the upper region of Seyhan and Ceyhan watersheds located in the south of Turkey. Model sensitivity analysis, calibration, and validation were performed using SWAT-CUP automatic calibration program and SUFI-2 algorithm. According to the model sensitivity analysis results, the most sensitive parameters in these basins have been seen as CN2, ALPHA_BNK, CH_K2, and GW_DELAY. In this study, 11 years (1994-2004) meteorological and eight years (1997-2004) observed flow data were used, three years for the model warm-up period, five years (1997-2001) for calibration, and three years (2002-2004) for validation. The model statistical performance was evaluated using the Nash Sutcliffe Efficiency (NSE) as the objective function. As the result of the model calibration and validation, the NSE value in the considered four sub-basins varied between 0,70 - 0,90. The results obtained in the study showed a relatively high correlation between the observed and simulated discharge data.