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Cryo-EM Single-Particle Analysis (SPA) L1-129

Scientific InstrumentationNear-atomic 3D reconstruction of frozen-hydrated biomoleculesδ=5 · challengingL_DAG = 3📋 Stub — not mineable
📋

Unclaimed Principle — open for contribution

This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.

To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.

Forward model E

Purified biomolecules (protein complexes) are vitrified in amorphous ice; a 200-300 keV electron beam through a direct-electron detector records weak-phase projection images of random-orientation particles; alignment + class-averaging + 3D reconstruction via inverse Radon (Fourier-slice theorem) yields a near-atomic density map.

L-DAG

L.project.euler -> L.modulate.ctf -> int.spatial
L.project.eulerL.modulate.ctfint.spatial

Well-posedness W

Existence:
true
Uniqueness:
unique 3D reconstruction when orientations cover SO(3) uniformly and N_particle large
Stability:
conditional
κ:
1000

Signal-to-noise per image ~0.01-0.1; collective averaging boosts SNR as sqrt(N). Mismatch: pose-estimation error, CTF miscalibration, conformational heterogeneity (multi-class), preferential orientation.

Solvability C

Solver class:
RELION / cryoSPARC / cisTEM iterative expectation-maximization for pose + reconstruction; learned (Topaz particle pick, ML3D heterogeneity)
Convergence rate q:
2
Complexity:
EM iteration O(N_particle * image_size * N_orient * N_class * iter); learned pickers O(N_particle * CNN_forward)

Specs (0)

No L2 specs registered yet for this principle.