Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA.
Biomolecules. 2022 Jul 23;12(8):1022. doi: 10.3390/biom12081022.
Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between-or specific shape on-individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, , to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and 1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (1 scores 0.86-0.95) under experimental noise conditions.
在细胞内，细胞骨架丝通常排列成松散对齐的束。这些纤维束足够密集，表现出一定的规则性和平均方向，然而，它们的包装不足以在单个纤维之间施加对称性或特定形状。这种中间规律性在计算上很难处理，因为单个纤维具有一定的方向自由度，但是，纤维密度彼此之间没有很好地分割（特别是在存在噪声的情况下，如在冷冻电子断层扫描中）。在本文中，我们开发了一种基于动态规划的框架， ，用于描述亚细胞成分的挑战性 3D 图谱中纤维的结构排列。假设断层扫描可以旋转，使得纤维沿平均方向取向，所提出的框架首先识别候选纤维段的局部种子点，然后使用动态规划算法从种子点开始生长。我们在模拟断层扫描上验证了我们框架的各种算法变体，这些模拟断层扫描紧密模拟了实验图谱的噪声和外观。由于我们在模拟断层扫描中知道真实情况，包括精度、召回率和 1 分数的统计分析使我们能够优化这种新方法的性能。我们发现，用于路径密度的双金字塔形累积方案优于直线累积。此外，正向和反向路径密度的乘法提供了一种有效的滤波器，可以将纤维密度提升到噪声水平以上。我们的测试结果是一种稳健的方法，可以预期在实验噪声条件下表现良好（1 分数 0.86-0.95）。
