Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions
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Abstract (arXiv)
Single particle cryo-electron microscopy (EM) is an increasingly popular method for determining the 3-D structure of macromolecules from noisy 2-D images of single macromolecules whose orientations and positions are random and unknown. One of the great opportunities in cryo-EM is to recover the structure of macromolecules in heterogeneous samples, where multiple types or multiple conformations are mixed together. Indeed, in recent years, many tools have been introduced for the analysis of multiple discrete classes of molecules mixed together in a cryo-EM experiment. However, many interesting structures have a continuum of conformations which do not fit discrete models nicely; the analysis of such continuously heterogeneous models has remained a more elusive goal. In this manuscript, we propose to represent heterogeneous molecules and similar structures as higher dimensional objects. We generalize the basic operations used in many existing reconstruction algorithms, making our approach generic in the sense that, in principle, existing algorithms can be adapted to reconstruct those higher dimensional objects. As proof of concept, we present a prototype of a new algorithm which we use to solve simulated reconstruction problems.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Apr 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2021Deep learning based mixed-dimensional GMM for characterizing variability in CryoEMcited by 4
- 2024Mixture of neural fields for heterogeneous reconstruction in cryo-EMcited by 2
- 2023Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EMcited by 1
- 2026SOLVAR: Fast covariance-based heterogeneity analysis with pose refinement for cryo-EMcited by 0
- 2026SOLVAR: Fast covariance-based heterogeneity analysis with pose refinement for cryo-EM19 Feb 2026
- 2024Mixture of neural fields for heterogeneous reconstruction in cryo-EM12 Dec 2024
- 2023Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM13 Mar 2023
- 2021Deep learning based mixed-dimensional GMM for characterizing variability in CryoEM25 Jan 2021