Tracing Randomly Oriented Filaments in a Simulated Actin Network Tomogram

The disordered nature of the actin network in Dictyostelium discoideum filopodia makes identifying filaments within noisy cryo-electron tomograms extremely challenging. In this work, we present a computationally efficient dynamic programming-based framework for tracing arbitrarily oriented actin filaments. Starting from locally determined seed points, it accumulates densities along paths of a particular length within 45° of the three Cartesian coordinate axes. This novel approach covers all possible orientations, so there is no need to assume a dominant direction as in earlier work. For each seed point, the path with the highest density value is selected, and it acts as a candidate filament segment (CFS) that is likely to form a part of a filament when it has a high path density value. The subsequent stages involve identifying groups of CFSs with high path densities by binning and merging them. The merging step considers the relative orientations and distances of CFSs to connect them. In addition, the CFSs are extended to fill the noise-induced gaps to some extent. In the current prototype software, we focused on the proof of the concept, using a noisy simulated tomogram with a known ground truth that closely mimics the appearance of an experimental map. We achieved an almost perfect precision score of 0.999, but this success came at the expense of a lower recall score 0.462 due to false negatives. We discuss the dependencies as well as the limitations of the current filament merging that need to be overcome to achieve a higher recall score in the future.