A mechanistic in silico model predicts mitotic events and effects of perturbation in budding yeasts belonging to Ascomycota and Basidiomycota. The model identifies distinct pathways based on the population of cytoplasmic microtubules and cortical dyneins as determinants of nuclear and spindle positioning in these phyla.

Kinetochore couples chromosome movement to dynamic microtubules, a process that is fundamental to mitosis in all eukaryotes but poorly understood. In vertebrates, spindle-kinetochore-associated (Ska1–3) protein complex plays an important role in this process. However, the proteins that stabilize Ska-mediated kinetochore-microtubule attachment remain unknown. Here we show that microtubule plus-end tracking protein EB1 facilitates Ska localization on microtubules in vertebrate cells. EB1 depletion results in a significant reduction of Ska1 recruitment onto microtubules and defects in mitotic chromosome alignment, which is also reflected in computational modelling. Biochemical experiments reveal that EB1 interacts with Ska1, facilitates Ska1-microtubule attachment and together stabilizes microtubules. Structural studies reveal that EB1 either with Ska1 or Ska complex forms extended structures on microtubule lattice. Results indicate that EB1 promotes Ska association with K-fibres and facilitates kinetochore-microtubule attachment. They also implicate that in vertebrates, chromosome coupling to dynamic microtubules could be mediated through EB1-Ska extended structures.

Cell division through proper spindle formation is one of the key puzzles in cell biology. In most mammalian cells, chromosomes spontaneously arrange to achieve a stable bipolar spindle during metaphase which eventually ensures proper segregation of the DNA into the daughter cells. In this paper, we present a robust three-dimensional mechanistic model to investigate the formation and maintenance of a bipolar mitotic spindle in mammalian cells under different physiological constraints. Using realistic parameters, we test spindle viability by measuring the spindle length and studying the chromosomal configuration. The model strikingly predicts a feature of the spindle instability arising from the insufficient intercentrosomal angular separation and impaired sliding of the interpolar microtubules. In addition, our model successfully reproduces chromosomal patterns observed in mammalian cells, when activity of different motor proteins is perturbed.

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. However, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and found that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into an agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell types, as well as to branching at the subcellular and tissue levels.

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell-types, as well as to branching at the subcellular and tissue levels.