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A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework’s utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
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Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells
Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential equation approach by generating typical system histories, instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch—that is, without recourse to black-box packages. In some cases, their results can also be compared directly with single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies involving gene expression and error correction, respectively. For gene expression, stochastic simulation results are compared with experimental data, an important research exercise for biophysics students. For error correction, several proofreading models are compared to find the minimal components necessary for sufficient accuracy in translation. Animations of the stochastic error correction models provide insight into the proofreading mechanisms. Code samples and resulting animations showing results are given in the online Supplemental Material .
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- Award ID(s):
- 1715823
- PAR ID:
- 10162639
- Date Published:
- Journal Name:
- The Biophysicist
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2578-6970
- Page Range / eLocation ID:
- 3
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.35459/tbp.2019.000101
