Computing chemical droplet neurons guides with Bashar Ibrahim plus more scholar news

Numerical simulation teaching and more science citations? In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov–Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow.

Diabetes is a major and growing public health challenge which threatens to overwhelm medical services in the future. Type 2 diabetes confers significant morbidity and mortality, most notably with target organ damage to the eyes, kidneys, nerves and heart. The magnitude of cardiovascular risk associated with diabetes is best illustrated by its position as a coronary heart disease risk equivalent. Complications related to neuropathy are also vast, often working in concert with vascular abnormalities and resulting in serious clinical consequences such as foot ulceration. Increased understanding of the natural history of this disorder has generated the potential to intervene and halt pathological progression before overt disease ensues, after which point management becomes increasingly challenging. The concept of prediabetes as a formal diagnosis has begun to be translated from the research setting to clinical practice.

Budding yeast asymmetric cell division relies upon the precise coordination of spindle orientation and cell cycle progression. The spindle position checkpoint (SPOC) is a surveillance mechanism that prevents cells with misoriented spindles from exiting mitosis. The cortical kinase Kin4 acts near the top of this network. How Kin4 kinase activity is regulated and maintained in respect to spindle positional cues remains to be established. Here, we show that the bud neck–associated kinase Elm1 participates in Kin4 activation and SPOC signaling by phosphorylating a conserved residue within the activation loop of Kin4. Blocking Elm1 function abolishes Kin4 kinase activity in vivo and eliminates the SPOC response to spindle misalignment. These findings establish a novel function for Elm1 in the coordination of spindle positioning with cell cycle progression via its control of Kin4. See additional information at Monitoring spindle orientation by Bashar Ibrahim.

For successful mitosis, metaphase has to be arrested until all centromeres are properly attached. The onset of anaphase, which is initiated by activating the APC, is controlled by the spindle assembly checkpoint MSAC. Mad2, which is a constitutive member of the MSAC, is supposed to inhibit the activity of the APC by sequestering away its co-activator Cdc20. Mad1 recruits Mad2 to unattached kinetochores and is compulsory for the establishment of the Mad2 and Cdc20 complexes. Recently, based on results from in vivo and in vitro studies, two biochemical models were proposed: the Template and the Exchange model. Here, we derive a mathematical description to compare the dynamical behaviour of the two models. Our simulation analysis supports the Template model. Using experimentally determined values for the model parameters, the Cdc20 concentration is reduced down to only about half.