The Nuclear Pore Complex (NPC) is the only passageway for macromolecules between the nucleus and cytoplasm and an important reference standard in microscopy: it is intrinsic to cells, has a high copy number, it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudo-atomic precision, however, observed NPC heterogeneities, such as varying diameters, elongated shapes, 9-fold symmetry, and irregular shapes, evidence a high degree of divergence from this average. Single Molecule Localization Microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis methods study NPC variability. However, the true picture of NPC variability is unknown and the biological function of this variability is poorly understood. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from variability of the underlying structure. This thesis introduces a pipeline that synthesizes ground truth datasets of structurally variable NPCs based on architectural models of the true NPC to benchmark image analysis methods and to help elucidate real-life NPC variability.

In this pipeline, N- or C-terminally tagged NPC proteins can be selected for single- or multi-channel 3D simulations of geometrically variable NPCs. The NPC is furthermore represented as a spring model such that arbitrary deforming forces, of freely definable magnitudes, simulate shapes that are irregular, yet sufficiently smooth. Such simulations allow one to compare image analysis methods based on the quality and quantity of data required to elucidate specific types of variability. A side-by-side comparison with real data ultimately tests hypotheses about underlying NPC variability. Two clustering approaches are compared on simulations of geometric NPC variability. Furthermore, synthetically replicating analyses of real NPC radii reveal that a range of simulated variability parameters can lead to previously observed results. Ultimately, this thesis highlights the need and offers a template, for close-to-biology simulations when ground truth is unavailable.