STRC Gamma-Poisson Transduction Model
Context
The STRC Dual-Vector vs Single-Vector Transduction note contains v1 model numbers (single 67.4%, dual 1.2%, ratio 56.5x). These were SUPERSEDED on 2026-03-19 by a more accurate Gamma-Poisson (negative binomial) model.
v1 numbers are WRONG — the receptor competition correction was double-counted.
The Problem with Simple Poisson
v1 model: simple Poisson distribution for virus uptake. Issue: assumes every cell in the cochlea has identical probability of being transduced. In reality, cells vary enormously in their accessibility to the viral vector (depth from injection site, aqueous flow, extracellular matrix density). This cell-to-cell heterogeneity violates the Poisson assumption.
Gamma-Poisson (Negative Binomial) Model
The correct model: viral uptake at each cell follows a Gamma-Poisson (negative binomial) distribution:
- λ: mean particles per cell
- k: overdispersion parameter (k → ∞ = pure Poisson, small k = high heterogeneity)
Calibration from Experimental Data
Two data points from Omichi et al. (2020):
- Single AAV2 via RWM: 83.9% OHC transduction (mouse)
- Dual AAV2 via RWM: 65.6% OHC transduction (mouse)
Fitted parameters:
- λ_mouse = 8.11 (mean particles per OHC)
- k = 0.734 (overdispersion = 11.1x — significant heterogeneity)
Human Scaling
Mouse → human scaling factors:
- Perilymph volume: mouse 2.5 µL → human 191 µL (76x)
- Injection fill ratio adjustment
- Net scaling: 0.613x
λ_human = 4.97, k_human = 0.734
New Predictions (Clinical Titer 3.75×10¹² GC/mL)
| Construct | OHC Transduction | OHC Count |
|---|---|---|
| Single-vector mini-STRC | 77.8% | 9,336 of 12,000 |
| Dual-vector (R=30% recombination) | 16.4% | 1,967 |
| Dual-vector (R=50% recombination) | 27.3% | 3,278 |
| Dual-vector (R=100% recombination, theoretical max) | 38.2% | 4,589 |
The 56.5x ratio from v1 was too extreme (double-counted receptor competition). The correct advantage is 2.8-4.7x depending on recombination efficiency. Still decisive — even best-case dual vector is 2x worse than single.
Why v1 Was Wrong
v1 explicitly penalized dual-vector for receptor competition (AAV-A and AAV-B fighting for the same surface receptors). But the calibration data itself (Omichi dual = 65.6%) ALREADY encodes that competition. Double-counting produced an artifactually extreme ratio.
The Gamma-Poisson v2 model calibrates directly to both data points simultaneously, so receptor competition is implicit in the fitted parameters.
Updated Site
- DualVector.astro: titer sweep table updated to v2 numbers
- MiniSTRC.astro (section 2): updated
- en.json (dualvec.* keys): updated
- Deployed: https://27add2f0.strc-egor-lol.pages.dev
Connections
- STRC Dual-Vector vs Single-Vector Transduction — v1 numbers in that note are now outdated; use v2 from this note
[about]Misha[applies]STRC Mini-STRC Single-Vector Hypothesis — quantifies clinical advantage[see-also]Poisson Process — mathematical hub for the Poisson / Gamma-Poisson / overdispersion machinery used here[see-also]PhISEM Granular Synthesis — cross-domain sibling: same non-homogeneous / mixture-Poisson pattern applied to grain impacts over time instead of AAV uptake per cell- Omichi et al. 2020 (PMC7270144)