  The ability of eukaryotic cells to squeeze through constrictions is limited
by the stiffness of their large and rigid nucleus. However, migrating cells are
often able to overcome this limitation and pass through constrictions much
smaller than their nucleus, a mechanism that is not yet understood. This is
what we address here through a data-driven approach using microfluidic devices
where cells migrate through controlled narrow spaces of sizes comparable to the
ones encountered in physiological situations. Stochastic Force Inference is
applied to experimental nuclear trajectories and nuclear shape descriptors,
resulting in equations that effectively describe this phenomenon of nuclear
translocation. By employing a model where the channel geometry is an explicit
parameter and by training it over experimental data with different sizes of
constrictions, we ensure that the resulting equations are predictive to other
geometries. Altogether, the approach developed here paves the way for a
mechanistic and quantitative description of dynamical cell complexity during
its motility.
