Key signaling nodes in mammary gland development and cancer: β-catenin

β-Catenin plays important roles in mammary development and tumorigenesis through its functions in cell adhesion, signal transduction and regulation of cell-context-specific gene expression. Studies in mice have highlighted the critical role of β-catenin signaling for stem cell biology at multiple stages of mammary development. Deregulated β-catenin signaling disturbs stem and progenitor cell dynamics and induces mammary tumors in mice. Recent data showing deregulated β-catenin signaling in metaplastic and basal-type tumors suggest a similar link to reactivated developmental pathways and human breast cancer. The present review will discuss β-catenin as a central transducer of numerous signaling pathways and its role in mammary development and breast cancer

Recurrent network dynamics reconciles visual motion segmentation and integration

In sensory systems, a range of computational rules are presumed to be implemented by neuronal subpopulations with different tuning functions. For instance, in primate cortical area MT, different classes of direction-selective cells have been identified and related either to motion integration, segmentation or transparency. Still, how such different tuning properties are constructed is unclear. The dominant theoretical viewpoint based on a linear-nonlinear feed-forward cascade does not account for their complex temporal dynamics and their versatility when facing different input statistics. Here, we demonstrate that a recurrent network model of visual motion processing can reconcile these different properties. Using a ring network, we show how excitatory and inhibitory interactions can implement different computational rules such as vector averaging, winner-take-all or superposition. The model also captures ordered temporal transitions between these behaviors. In particular, depending on the inhibition regime the network can switch from motion integration to segmentation, thus being able to compute either a single pattern motion or to superpose multiple inputs as in motion transparency. We thus demonstrate that recurrent architectures can adaptively give rise to different cortical computational regimes depending upon the input statistics, from sensory flow integration to segmentation