Model binding, uptake, fusion, escape, and import steps. Choose mechanistic or empirical mode for assay. Download clean reports and validate entry results today securely.
| Mode | Cells | Dose | Key inputs | Entry efficiency | Entries per cell (λ) |
|---|---|---|---|---|---|
| Mechanistic | 1,000,000 | MOI 5 | Bind 30%, Internalize 60%, Fuse 20%, Escape 10%, Import 40%, Viability 95% | 0.1368% | 0.00684 |
| Mechanistic | 250,000 | Particles 2,000,000 | Bind 45%, Internalize 50%, Fuse 30%, Escape 15%, Import 35%, Inhibition 20% | 0.7969% | 0.06375 |
| Empirical | 200,000 | Particles 1,000,000 | 8% infected, background 0.5%, detection 80% | 1.875% | 0.09375 |
| Empirical | 120,000 | PFU 10,000 (ratio 200) | Count 6,500, background 250, detection 90% | 3.472% | 0.08333 |
Binding efficiency represents the fraction of particles that attach to receptors during the exposure window. If you move binding from 10% to 30%, every downstream step is multiplied threefold. Record temperature, time, and cell line because these shift receptor availability and diffusion. If receptor blocking reduces binding by 50%, you should expect roughly a 50% drop in overall efficiency, before later bottlenecks. Use the calculator to compare conditions side by side carefully, and treat binding as a tunable parameter when testing entry inhibitors or receptor knockdowns.
The inoculum must be converted into “particles applied” before any efficiency estimate is meaningful. With 1,000,000 cells at MOI 5, the calculator uses 5,000,000 particles. If you only have PFU, add a particle-to-PFU ratio; for example, 50,000 PFU at 100 particles per PFU also becomes 5,000,000 particles.
After estimating entered particles, the tool reports λ (expected entries per cell) and the Poisson-based probability of at least one entry. If 6,840 particles enter across 1,000,000 cells, λ = 0.00684 and P(≥1) ≈ 0.68%. For intuition, λ = 1 gives P(≥1) ≈ 63.2%, and λ = 3 gives ≈ 95.0%. This helps you predict how sparse entry will look under microscopy or flow cytometry and guides how many cells you must analyze for robust statistics.
Mechanistic mode highlights where entry fails by showing expected particle counts at each stage. A single weak step dominates the product: improving fusion from 5% to 20% yields a fourfold gain in overall efficiency even if other steps are unchanged. Use this sensitivity logic to prioritize experiments, such as optimizing endosomal escape chemistry or adjusting uptake pathways with temperature shifts. Apply inhibition and viability factors last to keep controls interpretable.
Empirical mode starts from infected-cell readouts, subtracts background, and corrects for detection efficiency. Example: 8% infected in 200,000 cells is 16,000 positives; subtract 0.5% background (1,000) to get 15,000, then divide by 80% detection to estimate 18,750 productive entries. Dividing by 1,000,000 particles gives 1.875% productive efficiency for clear reporting. Export CSV or PDF to preserve assumptions alongside values.
It is the fraction of applied particles estimated to complete the defined entry endpoint. Mechanistic mode uses the product of step efficiencies. Empirical mode estimates productive entries from infected-cell signal, then normalizes by particles applied.
Use MOI when dose is defined per cell and cell counts are reliable. Use total particles when you directly quantify particles added. Use PFU with a particles-per-PFU ratio when plaque data are available but particle counts are not.
Start with measured or literature-supported estimates for your system, then refine using controls. Treat each step as conditional on the previous stage. If only one stage is uncertain, adjust it and observe the impact on overall efficiency and λ.
Inhibition is modeled as an overall fractional reduction applied after the stepwise product. For example, 20% inhibition multiplies efficiency by 0.80. This keeps the interpretation consistent when comparing conditions with and without inhibitors.
Detection efficiency corrects for missed positives due to assay sensitivity, gating thresholds, reporter maturation, or sampling loss. If detection is 80%, the calculator divides the corrected infected signal by 0.80 to estimate the underlying productive entries.
It converts λ into a single-cell expectation using a Poisson model. Low λ means most cells receive no entries, even if some entry occurs overall. Use it to plan sample sizes and decide whether single-hit conditions are likely.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.