Building high-resolution structural models of parasites to understand their biophysics
Self-supervised methods that handle both random and structured noise patterns in microscopy data
Deep learning workflows for pixel-level annotation of electron tomography volumes using U-Net architecture
Complete models of T. brucei cytoskeleton amenable for finite element analysis
All workflows, data, and models made available for collaborators studying parasite mechanics
The parasitic life cycle involves a multitude of physical interactions with the host microenvironment during stages of motility and adhesion. This requires optimal adaptation of the mechanical properties of the parasite to its environment.
The shape and elasticity of unicellular parasites such as Trypanosoma brucei are largely defined by their cytoskeleton, including a subpellicular array of microtubule filaments that forms a corset around the entire cell. How exactly the interaction between the beat of the flagellum and the mechanical response of the cell body gives rise to the intricate rotational motility patterns is not fully understood.
In this project, we build "virtual parasites" from high-resolution image data as the basis for a precise data-driven mechanical understanding of parasite biophysics.
A web-based platform bringing advanced machine learning and image analysis to researchers without requiring programming expertise. Features include:
A Python module extending Noise2Void to handle structured noise patterns commonly found in microscopy images. Key features:
This project is funded as part of the DFG Priority Programme SPP2332 "Physics of Parasitism".