Highlights d 3D mapping of kinetochore architecture in human RPE1 cells d Outer kinetochore (NDC80, Mad1, and RZZ) has high nematic order d NDC80 jackknives and KNL1 unravels upon loss of attachment and tension, respectively d Recruitment of Mad2 is only coupled to the occupancy sensor (NDC80)

A new method is proposed to numerically extract the diffusivity of a (typically nonlinear) diffusion equation from underlying stochastic particle systems. The proposed strategy requires the system to be in local equilibrium and have Gaussian fluctuations but it is otherwise allowed to undergo arbitrary out-of-equilibrium evolutions. This could be potentially relevant for particle data obtained from experimental applications. The key idea underlying the method is that finite, yet large, particle systems formally obey stochastic partial differential equations of gradient flow type satisfying a fluctuation-dissipation relation. The strategy is here applied to three classic particle models, namely independent random walkers, a zero-range process and a symmetric simple exclusion process in one space dimension, to allow the comparison with analytic solutions.

SummaryKinetochores are multi-protein machines that form dynamic attachments to microtubules and generate the forces for chromosome segregation. High-fidelity is ensured because kinetochores can monitor attachment status and tension, using this information to activate checkpoints and error correction mechanisms. To explore how kinetochores achieve this we used two and three colour subpixel fluorescence localisation to define how six protein subunits from the major kinetochore complexes CCAN, MIS12, NDC80, KNL1, RZZ and the checkpoint proteins Bub1 and Mad2 are organised in the human kinetochore. This reveals how the kinetochore outer plate is a liquid crystal-like system with high nematic order and largely invariant to loss of attachment or tension except for two mechanical sensors. Firstly, Knl1 unravelling relays tension and secondly NDC80 jack-knifes under microtubule detachment, with only the latter wired up to the checkpoint signalling system. This provides insight into how kinetochores integrate mechanical signals to promote error-free chromosome segregation.

Continuum modeling of dissipative processes in materials often relies on strong phenomenological assumptions, as their derivation from underlying atomistic/particle models remains a major long-standing challenge. Here we show that the continuum evolution equations of a wide class of dissipative phenomena can be numerically obtained (in a discretized form) from fluctuations via an infinite-dimensional fluctuation-dissipation relation. A salient feature of the method is that these continuum equations can be fully pre-computed, enabling macroscopic simulations of arbitrary admissible initial conditions, without the need of any further microscopic simulations. We test this coarse-graining procedure on a one-dimensional non-linear diffusive process with known analytical solution, and obtain an excellent agreement for the density evolution. This illustrative example serves as a blueprint for a new multiscale paradigm, where full dissipative evolution equations-and not only parameters-can be numerically computed from lower scale data.