Intracellular signaling and trafficking in cancer: Role of Rab5-GTPases in migration and invasion of breast cancer cells

Metastasis is characterized pathologically by uncontrolled cell invasion, proliferation, migration and angiogenesis. Steroid hormones, such as estrogen, and growth factors, which include insulin growth factor I/II (IGF-1/IGF-2) therapy has been associated with most if not all of the features of metastasis. It has been determined that IGF-1 increases cell survival of cancer cells and potentiate the effect of E2 and other ligand growth factors on breast cancer cells. However not much information is available that comprehensively expounds on the roles of insulin growth factor receptor (IGFR) and Rab GTPases may play in breast cancer. The latter, Rab GTPases, are small signaling molecules and critical in the regulation of many cellular processes including cell migration, growth via the endocytic pathway. This research involves the role of Rab GTPases, specifically Rab5 and its guanine exchange factors (GEFs), in the promotion of cancer cell migration and invasion. Two important questions abound: Are IGFR stimulation and downstream effect involved the endocytic pathway in carcinogenesis? What role does Rab5 play in cell migration and invasion of cancer cells? The hypothesis is that growth factor signaling is dependent on Rab5 activity in mediating the aggressiveness of cancer cells. The goal is to demonstrate that IGF-1 signaling is dependent on Rab5 function in breast cancer progression. Here, the results thus far, have shown that while activation of Rab5 may mediate increased cell proliferation, migration and invasion in breast cancer cells, the Rab5 GEF, RIN1 interacts with the IGFR thereby facilitating migration and invasion activities in breast cells. Furthermore, endocytosis of the IGFR in breast cancer cells seems to be caveolin dependent as the data has shown. This taken together, the data shows that IGF-1 signaling in breast cancer cells relies on IGF-1R phosphorylation, caveolae internalization and sequestration to the early endosome RIN1 function and Rab5 activation.^

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.