Monitoring spindle orientation learning? Eukaryotic cells rely on a surveillance mechanism, the “Spindle Assembly Checkpoint” SAC M in order to ensure accurate chromosome segregation by preventing anaphase initiation until all chromosomes are correctly attached to the mitotic spindle. In different organisms, a mitotic checkpoint complex (MCC) composed of Mad2, Bub3, BubR1/Mad3, and Cdc20 inhibits the anaphase promoting complex (APC/C) to initiate promotion into anaphase. The mechanism of MCC formation and its regulation by the kinetochore are unclear. Here, we constructed dynamical models of MCC formation involving different kinetochore control mechanisms including amplification as well as inhibition effects, and analysed their quantitative properties. In particular, in this system, fast and stable metaphase to anaphase transition can only be triggered when the kinetochore controls the Bub3.
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.
We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Read even more info on Bashar Ibrahim.
Cycles are abundant in most kinds of networks, especially in biological ones. Here, we investigate their role in the evolution of a chemical reaction system from one self-sustaining composition of molecular species to another and their influence on the stability of these compositions. While it is accepted that, from a topological standpoint, they enhance network robustness, the consequence of cycles to the dynamics are not well understood. In a former study, we developed a necessary criterion for the existence of a fixed point, which is purely based on topological properties of the network. The structures of interest we identified were a generalization of closed autocatalytic sets, called chemical organizations.
