Gas vesicles, micrometer-scale protein structures that function as bacterial buoyancy providers, encapsulate gas in a highly optimized manner. While their atomic structure has been elucidated through single-particle analysis of cryo-EM images, certain structural and functional details remain uncertain. Its biogenesis - the formation and growth mechanisms - consequently remains elusive. Here we apply automated segmentation methods originating from cell imaging to cryo-EM images of gas vesicles to analyze the positions of gas vesicle features in a context-preserving matter. Subsequent whole gas vesicle processing is able to transform accurate gas vesicle segmentations into high-confidence structural feature location picks and statistics. This enables the formation of a sizeable data set containing 86k whole gas vesicles, improved resolution 2D class averages, and the potential for improved structural modeling. Combining high sample number contextual information enables inference on the dynamical properties of gas vesicle growth. Our findings validate recent atomic structure propositions and lend support to a stochastic monomer insertion growth model.

Cyo-EM is a powerful technique in biophysics to model. By recording tens of thousands of these images and using computational methods to form a 3D density map. The interpretability of these cryo-EM maps decreases in high-resolution by using map sharpening to increase these areas of low contrast locally. It is possible to locally sharpen the cryo-EM map. While global sharpening methods would result in oversharpening of some areas should be and undersharpening in others, using a local sharpening method can adjust for a variation in contrast within a map. This resulted in the development of LocScale. Which is a local sharpening method using a (pseudo) atomic model. The main drawback of using a local sharpening method which would require an atomic model is the fact that it is impractical in cases where there is no model available as well as requiring atomic model refinement for sharpening, which takes a lot of resources. While other machine learning methods are able to be used to sharpen EM-maps. When decomposing the sharpened maps in Fourier space, it often becomes clear that not only the amplitudes but also the phases, instead of just the amplitudes of the EM-map have been changed. Resulting in hallucinations appearing, and ML sharpening methods are often implemented in such a way that the entire method is housed inside a black box, making it challenging to understand why certain parts of the macromolecule are sharpened. Using a machine learning-based approach to predicting local radial profiles it is possible to limit the use of machine learning to only one step, the step for determining the radial profiles, compared to the model-based method, LocScale. And use all other steps which would normally be associated with LocScale, vastly reducing the use of ML. Since the radial profiles describe the amplitudes of the EM-map it is possible to change these while leaving the phases unchanged. Reducing the chance of hallucinations appearing to zero.In this thesis, it is shown that it is possible to produce a machine learning method (named iLocScale) to effectively sharpen EM maps by predicting radial profiles. This resulted in a method which is almost as sharp as their model-based (target) counterpart (LocScale) in the modelled region and vastly outperforms LocScale in the unmodelled regions for sharpness. While also offering a large improvement in correlation coefficient for local resolution and B-factor between the iLocScale sharpened maps (0.3416) and unsharpened maps (0.0465). Moreover, iLocScale vastly outperforms LocScale in unmodeled regions. It also takes approximately 75% less time.