midSIN

midSIN computes the Specific INfection (SIN) concentration from endpoint dilution assay data using Bayesian inference to provide accurate viral infectivity measurements.

Key Features:

• SIN estimation: Computes the Specific INfection (SIN) concentration expressed as infections per milliliter from endpoint dilution assay readouts.
• Bayesian inference: Uses Bayesian inference to estimate SIN from binary or categorical endpoint dilution outcomes.
• Comparison to classical methods: Accounts for and corrects biases present in Reed–Muench and Spearman–Kärber approximations used to derive ID50/TCID50.
• MOI equivalence: Reports SIN as a measure corresponding to multiplicity of infection (MOI), analogous to plaque or focus-forming units (PFU/FFU).
• Experimental compatibility: Operates on standard endpoint dilution assays without requiring changes to existing experimental protocols.
• Plate design analysis: Quantifies the impact of endpoint dilution plate design choices, including dilution factors and replicates per dilution, on measurement accuracy.

Scientific Applications:

• Viral infectivity quantification: Accurate quantification of viral infectivity in samples via SIN concentration.
• MOI-guided experimental planning: Informing infection setup by providing MOI-equivalent SIN values for experimental design.
• Assay optimization: Evaluating and optimizing endpoint dilution plate parameters (dilution factors, replicate counts) to improve measurement precision.
• Demonstrated viruses: Applied to influenza and respiratory syncytial virus samples to demonstrate measurement accuracy.

Methodology:

Applies Bayesian inference to endpoint dilution assay data to estimate Specific INfection (SIN) concentration, compares estimates to Reed–Muench and Spearman–Kärber approximations, and analyzes the effects of dilution factors and replicate counts on measurement accuracy.