Target Transduction MOI

Invert Poisson single-hit transduction probability to estimate an MOI for a supplied target fraction; susceptibility heterogeneity is not modeled.

Description

Invert the Poisson single-hit model to calculate the mean multiplicity corresponding to a supplied target transduced fraction.

Multiplicity of infection, or MOI, describes an average number of infectious events per target cell in the model used here. An average does not mean that every cell receives the same number of events. A Poisson model represents independent events distributed across equally susceptible cells, allowing some cells to receive none and others to receive more than one.^[1]

Under a single-hit model, a cell counts as transduced when it receives at least one successful event. If the Poisson mean is m, the probability of no event is exp(−m), so the fraction with one or more is 1 − exp(−m). Inverting that relationship gives the mean corresponding to a target fraction. This is a model calculation rather than an experimentally measured response curve.

Target Transduction MOI takes a fraction between zero and one, excluding one, and returns the required model MOI. It does not require a stock titer, cell count, or inoculum volume.

When to translate a target fraction into a model MOI

Use the tool to explore the Poisson relationship or check a calculation based on an already selected target fraction. Treat the result as conditional on the single-hit, equal-susceptibility assumptions.

Compare the mean-event requirements associated with several target fractions.
Understand why higher coverage requires progressively larger model MOI.
Check the distinction between a fraction with at least one event and the average events per cell.

How to enter a target fraction and invert the Poisson model

Enter Target transduced fraction as a number from 0 up to, but not including, 1. For example, 0.9 means 90%.
Run the tool and read Required moi as a dimensionless mean number of events per cell.
Compare the result with the assumptions and experimental measurements relevant to the system; the tool does not calculate an inoculum volume.

targetTransducedFraction = 1 − exp(−m); requiredMoi = −ln(1 − targetTransducedFraction)

The logarithm is natural. The implementation evaluates −Math.log1p(−fraction), which avoids losing precision when the supplied fraction is small.

The input is checked as a finite fraction in [0, 1], and exactly 1 is then rejected because it requires an infinite Poisson mean. A zero target is accepted and gives zero MOI. Invalid fractions produce an input error rather than an infinite output.

json

{
  "input": {
    "targetTransducedFraction": 0.9
  },
  "output": {
    "requiredMoi": 2.302585092994046
  }
}

Target fraction	Model MOI or behavior
0	0
0.5	Approximately 0.693147
0.9	Approximately 2.302585
0.99	Approximately 4.605170
1	Input error: no finite mean gives complete coverage.

The model counts cells with one or more events. It does not calculate a target for exactly one event, nor does it limit multiple events in cells counted as transduced.

Limitations of the single-hit transduction model

Equal susceptibility and independent events are assumed. Receptor variation, aggregation, cell-state differences, and other departures from that model are not represented.
Measured reporter expression or another transduction endpoint may differ from the model event probability.
No stock-titer conversion, dose-volume calculation, viability effect, uncertainty interval, or experimental optimization is performed.
The model MOI grows without bound as the target approaches one. A large result does not establish an achievable or suitable experimental condition.

Alternative or Complementary titer and cell-count calculations

When using a measured transduced fraction to estimate functional titer, include cell count, dilution, and inoculum volume in a dedicated titer calculation. A separate inoculum-to-cell-count tool uses supplied PFU and MOI; those assay units must be appropriate to the system before connecting the calculations.

References

The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays — Biophysical Journal / Biophysical Society

Similar or alternative tools

Poisson Corrected Lentiviral Titer
Use a measured transduced fraction, target cells, dilution fraction, and volume to estimate MOI and transducing units per mL.
Cells For Inoculum MOI
Calculate target cell count from supplied stock PFU/mL, inoculum volume, dilution fraction, and positive target MOI.