Calculate Multiplicity of Infection for viral transductions. Determine virus volume for target MOI or effective MOI from a given volume. Includes Poisson-based infection probability estimates.
Fill in the form and click “Calculate” to see your MOI calculations.
Multiplicity of Infection (MOI) is the ratio of infectious agents (viruses, bacteriophages, or other infectious particles) to target cells in a given experiment. It represents the average number of virus particles that each cell is exposed to during infection. MOI is a critical parameter in virology, gene therapy, molecular biology, and biotechnology experiments. Understanding MOI is crucial for students studying virology, cell biology, gene therapy, and biotechnology, as it explains how to design viral transduction experiments, calculate virus volumes needed, and predict infection efficiency. MOI concepts appear in virtually every viral transduction protocol and are foundational to understanding viral infection, gene delivery, and experimental design.
Poisson statistics govern viral infection because virus particles randomly encounter cells. This means not every cell receives the same number of particles even at a given MOI. At MOI = 1, while on average each cell receives one particle, some cells receive zero (≈36.8% uninfected), while others receive two or more. The fraction of cells uninfected follows: P(0) = e^(-MOI). The fraction infected at least once follows: P(≥1) = 1 - e^(-MOI). Understanding Poisson statistics helps you see why MOI 1 doesn't infect all cells, why higher MOIs are needed for complete infection, and how to predict infection efficiency.
Virus titer is the concentration of infectious particles per milliliter, expressed in different units depending on virus type: TU/mL (Transducing Units) for lentivirus, vg/mL (viral genomes) for AAV, pfu/mL (plaque-forming units) for adenovirus. Physical particle counts (vg, genome copies) may be 10-1000× higher than infectious titers (TU, pfu) depending on virus quality. Understanding titer units helps you use the correct values in calculations and interpret experimental results correctly.
MOI selection depends on experimental goals. For clonal selection (single integrations), use MOI 0.1-0.3 where most cells remain uninfected. For bulk transductions, use MOI 1-3 for good balance between efficiency and avoiding multiple integrations. For maximum infection, use MOI 5-10, but watch for cytotoxicity. MOI >10 is generally avoided except for specific applications. Understanding MOI selection helps you design experiments that achieve your goals while avoiding cytotoxicity and multiple integrations.
Volume calculations determine how much virus stock to add. The formula is: Volume (µL) = (MOI × Cell Count) / (Titer per mL) × 1000. This calculation ensures you add the correct number of infectious particles to achieve your target MOI. Understanding volume calculations helps you prepare virus stocks correctly and achieve desired infection rates.
This calculator is designed for educational exploration and practice. It helps students master MOI calculations by determining virus volumes for target MOI, calculating effective MOI from given volumes, and predicting infection efficiency using Poisson statistics. The tool provides step-by-step calculations showing how to account for cell count, virus titer, and Poisson distribution. For students preparing for virology exams, gene therapy courses, or biotechnology labs, mastering MOI is essential—these calculations appear in virtually every viral transduction protocol and are fundamental to experimental success. The calculator supports both calculation modes, helping students understand all aspects of MOI calculations.
Critical disclaimer: This calculator is for educational, homework, and conceptual learning purposes only. It helps you understand MOI theory, practice volume calculations, and explore viral transduction planning. It does NOT provide instructions for actual viral transduction procedures, which require proper training, biosafety protocols, sterile technique, and adherence to validated laboratory procedures. Never use this tool to determine actual viral transduction protocols, prepare virus stocks for experiments, or make decisions about viral infection conditions without proper laboratory training and supervision. Real-world viral transduction involves considerations beyond this calculator's scope: biosafety levels, cell type-specific requirements, virus pseudotype selection, transduction enhancers, and empirical verification. Use this tool to learn the theory—consult trained professionals and validated protocols for practical applications.
Multiplicity of Infection (MOI) is the ratio of infectious particles to target cells: MOI = Total Virus Particles / Total Cells. It represents the average number of virus particles that each cell is exposed to during infection. MOI is critical because it determines infection efficiency, affects the number of viral integrations per cell, and influences cytotoxicity. Understanding MOI helps you design experiments that achieve desired infection rates while avoiding cytotoxicity and multiple integrations.
Viral infection follows a Poisson distribution because virus particles randomly encounter cells. This means not every cell receives the same number of particles even at a given MOI. The fraction of cells uninfected is: P(0) = e^(-MOI). The fraction infected at least once is: P(≥1) = 1 - e^(-MOI). At MOI = 1, ≈36.8% of cells remain uninfected, while ≈63.2% receive at least one particle. Understanding Poisson statistics helps you see why MOI 1 doesn't infect all cells and why higher MOIs are needed for complete infection.
Virus volume is calculated as: Volume (µL) = (MOI × Cell Count) / (Titer per mL) × 1000. For example, to achieve MOI 1 with 100,000 cells and titer 10⁸ TU/mL: Volume = (1 × 100,000) / (10⁸) × 1000 = 1 µL. Understanding this calculation helps you determine how much virus stock to add to achieve your target MOI.
MOI is calculated as: MOI = (Volume µL × Titer per mL) / (Cell Count × 1000). For example, if you add 5 µL of virus (titer 10⁸ TU/mL) to 50,000 cells: MOI = (5 × 10⁸) / (50,000 × 1000) = 10. Understanding this calculation helps you determine the effective MOI when you know the volume added.
Different virus types use different titer units: Lentivirus uses TU/mL (Transducing Units) or IFU/mL (Infectious Units), AAV uses vg/mL (viral genomes) or GC/mL (genome copies), Adenovirus uses pfu/mL (plaque-forming units) or IU/mL, Retrovirus uses TU/mL or CFU/mL (colony-forming units). Physical particle counts (vg, genome copies) are typically 10-1000× higher than infectious titers (TU, pfu) because not all particles are infectious. Understanding titer units helps you use the correct values in calculations and interpret experimental results.
MOI selection depends on experimental goals: MOI 0.1-0.3 for clonal selection (single integrations), MOI 1-3 for bulk transductions (good balance), MOI 5-10 for maximum infection (watch for cytotoxicity), MOI >10 generally avoided (high risk of cell death). Understanding MOI selection helps you design experiments that achieve your goals while avoiding cytotoxicity and multiple integrations.
MOI provides a theoretical estimate, but actual infection efficiency depends on: cell type and surface receptor expression, cell cycle stage and health, virus pseudotype (VSV-G, native envelope, etc.), transduction enhancers (polybrene, spinoculation, RetroNectin), virus age and storage conditions (titers degrade), and volume of medium (higher volume dilutes virus). Understanding these factors helps you optimize transduction conditions and interpret experimental results correctly.
This interactive tool helps you calculate MOI and virus volumes for viral transduction experiments. Here's a comprehensive guide to using each feature:
Choose your calculation mode:
Volume from MOI
Use this mode when you know your target MOI and want to calculate how much virus volume to add. Enter: target MOI, cell count, and virus titer.
MOI from Volume
Use this mode when you know the volume you're adding and want to calculate the effective MOI. Enter: virus volume, cell count, and virus titer.
Enter the number of target cells:
Cell Count
Enter the total number of cells you plan to transduce. This is typically determined by cell counting (hemocytometer or automated counter) before transduction.
Enter your virus titer:
Virus Titer
Enter the concentration of infectious particles per milliliter (e.g., TU/mL, vg/mL, pfu/mL). Use the titer unit that matches your virus type and experimental endpoint. Ensure you're using functional titer (not just physical particle count) for accurate MOI calculations.
Enter the appropriate value based on your selected mode:
Target MOI (Volume from MOI mode)
Enter your desired MOI value. Typical ranges: 0.1-0.3 (clonal selection), 1-3 (bulk transduction), 5-10 (maximum infection).
Virus Volume (MOI from Volume mode)
Enter the volume of virus you plan to add in µL. The calculator will determine the effective MOI.
Click "Calculate" to get your results:
View Calculation Results
The calculator shows: (a) Effective MOI, (b) Required virus volume (µL), (c) Total virus particles added, (d) Fraction of cells uninfected (Poisson), (e) Fraction of cells infected at least once (Poisson), (f) Notes about MOI interpretation, (g) Summary of results.
Check Infection Predictions
Review the Poisson-based predictions for fraction uninfected and fraction infected. These help you understand expected infection efficiency.
Example: Calculate volume for MOI 1 with 100,000 cells and titer 10⁸ TU/mL
Input: Mode = Volume from MOI, Target MOI = 1, Cell Count = 100,000, Titer = 10⁸ TU/mL
Output: Volume = 1 µL, Effective MOI = 1.0, ~36.8% uninfected, ~63.2% infected
Explanation: Calculator uses MOI formula to determine volume, then applies Poisson statistics to predict infection efficiency.
Understanding the mathematics empowers you to calculate MOI on exams, verify calculator results, and build intuition about viral transduction.
MOI = Total Virus Particles / Total Cells
Where:
MOI = Multiplicity of Infection (dimensionless ratio)
Total Virus Particles = number of infectious particles added
Total Cells = number of target cells
Key insight: MOI is a ratio that represents the average number of virus particles per cell. MOI = 1 means on average each cell receives one particle, but Poisson statistics mean some cells receive zero while others receive multiple. Understanding this helps you see why MOI is an average, not a guarantee.
To determine how much virus to add:
Volume (µL) = (MOI × Cell Count) / (Titer per mL) × 1000
Where:
Volume = virus volume in microliters
MOI = target multiplicity of infection
Cell Count = number of target cells
Titer per mL = infectious particles per milliliter
× 1000 = conversion factor (mL to µL)
To determine effective MOI from volume added:
MOI = (Volume µL × Titer per mL) / (Cell Count × 1000)
This rearranges the volume formula to solve for MOI.
The probability that a cell receives zero particles:
P(0) = e^(-MOI)
Where e ≈ 2.718 is Euler's number. This follows from Poisson distribution for random particle encounters.
Example: At MOI = 1, P(0) = e^(-1) ≈ 0.368 (36.8% uninfected)
The probability that a cell receives at least one particle:
P(≥1) = 1 - e^(-MOI)
This is the complement of P(0), since a cell either receives zero or at least one particle.
Example: At MOI = 1, P(≥1) = 1 - e^(-1) ≈ 0.632 (63.2% infected)
Given: Target MOI = 1, Cell Count = 100,000, Titer = 10⁸ TU/mL
Find: Virus volume needed
Step 1: Apply volume formula
Volume (µL) = (MOI × Cell Count) / (Titer per mL) × 1000
Volume = (1 × 100,000) / (10⁸) × 1000
Volume = 100,000 / 10⁸ × 1000
Volume = 1 µL
Step 2: Calculate infection predictions
P(0) = e^(-1) ≈ 0.368 (36.8% uninfected)
P(≥1) = 1 - e^(-1) ≈ 0.632 (63.2% infected)
Given: Virus Volume = 5 µL, Cell Count = 50,000, Titer = 10⁸ TU/mL
Find: Effective MOI
Step 1: Apply MOI formula
MOI = (Volume µL × Titer per mL) / (Cell Count × 1000)
MOI = (5 × 10⁸) / (50,000 × 1000)
MOI = 5 × 10⁸ / 5 × 10⁷
MOI = 10
Step 2: Calculate infection predictions
P(0) = e^(-10) ≈ 0.000045 (0.0045% uninfected)
P(≥1) = 1 - e^(-10) ≈ 0.999955 (99.9955% infected)
Understanding MOI is essential for students across virology and biotechnology coursework. Here are detailed student-focused scenarios (all conceptual, not actual viral transduction procedures):
Scenario: Your virology homework asks: "How much virus (µL) do you need to achieve MOI 1 with 100,000 cells and titer 10⁸ TU/mL?" Use the calculator: enter Volume from MOI mode, MOI = 1, Cell Count = 100,000, Titer = 10⁸. The calculator shows: Volume = 1 µL, Effective MOI = 1.0, ~36.8% uninfected, ~63.2% infected. You learn: how to calculate volume from MOI and how Poisson statistics predict infection efficiency. The calculator helps you check your work and understand each step.
Scenario: Your gene therapy lab report asks: "Explain why MOI 1 doesn't infect all cells." Use the calculator: compare infection predictions at different MOIs. Understanding this helps explain Poisson statistics, why random particle encounters lead to variable infection, and why higher MOIs are needed for complete infection. The calculator helps you verify your understanding and see how MOI affects infection efficiency.
Scenario: An exam asks: "You add 5 µL of virus (titer 10⁸ TU/mL) to 50,000 cells. What is the effective MOI?" Use the calculator: enter MOI from Volume mode, Volume = 5 µL, Cell Count = 50,000, Titer = 10⁸. The calculator calculates: Effective MOI = 10, ~0.0045% uninfected, ~99.9955% infected. This demonstrates how to calculate MOI from known volumes.
Scenario: Problem: "Compare infection efficiency at MOI 0.1, 1, 3, and 10 for 100,000 cells with titer 10⁸ TU/mL." Use the calculator: enter each MOI value. The calculator shows how infection efficiency increases with MOI: MOI 0.1 (~9.5% infected), MOI 1 (~63.2% infected), MOI 3 (~95.0% infected), MOI 10 (~99.9955% infected). This demonstrates how MOI selection affects infection efficiency.
Scenario: Your biotechnology homework asks: "Why use low MOI (0.1-0.3) for clonal selection?" Use the calculator: enter MOI 0.1. The calculator shows: ~90.5% uninfected, ~9.5% infected. Understanding this helps explain why low MOI minimizes multiple integrations, why most cells remain uninfected, and why clonal selection requires low MOI. The calculator makes this relationship concrete—you see exactly how low MOI affects infection distribution.
Scenario: Problem: "You have AAV with titer 10¹² vg/mL (viral genomes). How much do you need for MOI 1 with 100,000 cells?" Use the calculator: enter MOI = 1, Cell Count = 100,000, Titer = 10¹². The calculator calculates: Volume = 0.1 µL. This demonstrates how high titer (vg/mL) requires very small volumes, and why physical particle counts differ from infectious titers.
Scenario: Your instructor recommends practicing different types of MOI problems. Use the calculator to work through: (1) Volume from MOI calculations, (2) MOI from volume calculations, (3) Different MOI values, (4) Different cell counts, (5) Different titers. The calculator helps you practice all problem types, identify common mistakes, and build confidence. Understanding how to solve different types of MOI problems prepares you for exams where you might encounter various scenarios.
MOI problems involve volume calculations, titer conversions, and Poisson statistics that are error-prone. Here are the most frequent mistakes and how to avoid them:
Mistake: Using titer in per mL but forgetting to multiply by 1000 to convert volume to µL, or vice versa.
Why it's wrong: The formula Volume (µL) = (MOI × Cell Count) / (Titer per mL) × 1000 includes the × 1000 conversion factor. If you forget it, you get volume in mL instead of µL, which is 1000× too large. For example, if you calculate 0.001 mL but forget conversion, you might think you need 0.001 µL (wrong) instead of 1 µL (correct).
Solution: Always remember the × 1000 conversion factor when calculating volume in µL. The formula explicitly includes it: Volume (µL) = ... × 1000. The calculator does this automatically—observe it to reinforce the conversion.
Mistake: Using vg/mL (viral genomes) or physical particle counts when you should use TU/mL (transducing units) or pfu/mL (plaque-forming units).
Why it's wrong: Physical particle counts (vg/mL) are typically 10-1000× higher than infectious titers (TU/mL) because not all particles are infectious. Using physical counts gives wrong MOI—you'll add too much virus. For example, if titer is 10¹² vg/mL but only 10⁹ TU/mL, using vg/mL gives 1000× too much virus.
Solution: Always use functional titer (TU, pfu, IFU) for MOI calculations, not physical particle counts. Use vg/mL only if your experimental endpoint measures physical particles, not infection. The calculator emphasizes this—use it to reinforce correct titer usage.
Mistake: Thinking that MOI 1 means every cell receives exactly one particle and all cells are infected.
Why it's wrong: Viral infection follows Poisson statistics. At MOI 1, while on average each cell receives one particle, some cells receive zero (≈36.8% uninfected) while others receive multiple. Only ≈63.2% of cells receive at least one particle. Assuming all cells are infected gives wrong expectations.
Solution: Always remember Poisson statistics: P(≥1) = 1 - e^(-MOI). At MOI 1, only ~63% are infected. To infect >95% of cells, you need MOI ≥ 3. The calculator shows Poisson predictions—use them to reinforce correct expectations.
Mistake: Using the wrong formula for the calculation mode (e.g., using volume formula when you should calculate MOI from volume).
Why it's wrong: There are two modes: (1) Volume from MOI: Volume = (MOI × Cell Count) / (Titer) × 1000, (2) MOI from Volume: MOI = (Volume × Titer) / (Cell Count × 1000). Using the wrong formula gives wrong results. For example, if you know volume and want MOI, using the volume formula doesn't solve for MOI.
Solution: Always identify what you're solving for: volume or MOI. Use the appropriate formula. The calculator has two modes—use them to reinforce which formula to use when.
Mistake: Forgetting to include cell count in the formula, or using wrong cell count (e.g., cells per well instead of total cells).
Why it's wrong: MOI depends on both virus particles and cell count. If you forget cell count, you can't calculate MOI or volume correctly. Using cells per well when you need total cells gives wrong results if you're seeding multiple wells.
Solution: Always include cell count in calculations. If seeding multiple wells, use total cells (cells per well × number of wells). The calculator requires cell count—use it to reinforce this requirement.
Mistake: Using P(0) = e^(MOI) instead of e^(-MOI), or forgetting the negative sign.
Why it's wrong: The Poisson formula for uninfected cells is P(0) = e^(-MOI), with a negative exponent. Using positive exponent gives wrong probabilities. For example, at MOI = 1, e^(-1) ≈ 0.368 (correct), but e^(1) ≈ 2.718 (wrong, probability can't be >1).
Solution: Always remember the negative sign: P(0) = e^(-MOI). The negative exponent is crucial—it ensures probabilities decrease as MOI increases. The calculator uses the correct formula—observe it to reinforce the negative sign.
Mistake: Thinking that MOI = 1 means every cell receives exactly one particle.
Why it's wrong: MOI is an average. At MOI = 1, the average is one particle per cell, but Poisson statistics mean some cells receive zero, some receive one, some receive two, etc. The distribution follows Poisson: P(k) = (MOI^k × e^(-MOI)) / k!. Understanding this helps you see why infection efficiency is less than 100% even at MOI 1.
Solution: Always remember that MOI is an average, not a guarantee. Poisson statistics determine actual distribution. The calculator shows Poisson predictions—use them to reinforce that MOI is an average.
Once you've mastered basics, these advanced strategies deepen understanding and prepare you for complex viral transduction planning:
Conceptual insight: Viral infection follows Poisson statistics because virus particles randomly encounter cells. The Poisson distribution describes random events occurring independently. In viral infection, each particle has an equal probability of encountering any cell, leading to random distribution. Understanding this provides deep insight beyond memorization: MOI predictions are probabilistic, not deterministic, because infection is a random process.
Quantitative insight: Common MOI ranges: 0.1-0.3 (clonal selection), 1-3 (bulk transduction), 5-10 (maximum infection), >10 (generally avoided). Memorizing these helps you quickly choose appropriate MOIs. Understanding these patterns provides quantitative insight into why certain MOIs are used for specific applications.
Practical framework: Always follow this order: (1) Identify what you're solving for (volume or MOI), (2) Select appropriate formula, (3) Substitute values with correct units, (4) Calculate result, (5) Check units and reasonableness, (6) Apply Poisson statistics to predict infection efficiency. This systematic approach prevents mistakes and ensures you don't skip steps. Understanding this framework builds intuition about MOI calculations.
Unifying concept: MOI is fundamental to gene therapy (viral vectors), biotechnology (protein production), and research (gene editing, overexpression). Understanding MOI helps you see why viral vectors are used for gene delivery, how MOI affects transgene expression, and why MOI optimization is critical for experimental success. This connection provides context beyond calculations: MOI is essential for modern biotechnology.
Exam technique: For quick estimates: MOI 1 → ~63% infected, MOI 3 → ~95% infected, MOI 5 → ~99.3% infected. For volume: MOI 1 with 10⁵ cells and 10⁸ titer ≈ 1 µL. These mental shortcuts help you quickly estimate on multiple-choice exams and check calculator results. Understanding approximate relationships builds intuition about MOI values.
Advanced consideration: This calculator provides theoretical predictions based on Poisson statistics. Real systems show: (a) Cell type affects actual infection (receptor expression), (b) Virus pseudotype affects efficiency, (c) Transduction enhancers (polybrene) improve infection, (d) Virus age and storage affect titer, (e) Experimental conditions (volume, time) affect results. Understanding these limitations shows why empirical optimization is often needed, and why advanced methods are required for accurate work in research, especially for novel cell types or experimental conditions.
Advanced consideration: Higher MOI increases the probability of multiple viral integrations per cell. At MOI 1, most infected cells receive one particle. At MOI 10, many cells receive multiple particles. Multiple integrations can cause: (a) Higher transgene expression, (b) Increased risk of insertional mutagenesis, (c) Potential cytotoxicity. Understanding this helps you balance infection efficiency with integration copy number, which is important for gene therapy and research applications.
• Poisson Distribution Model: Infection predictions assume viral particles distribute randomly according to Poisson statistics. This works for well-mixed, dilute conditions but may not hold for high-concentration virus, receptor-limited scenarios, or non-random viral attachment patterns.
• Titer Accuracy Critically Important: MOI calculations are only as accurate as your viral titer measurement. Titers can vary significantly between quantification methods (plaque assay, p24 ELISA, qPCR) and may not reflect infectious particle concentration. Functional titers are preferred but harder to obtain.
• Cell-Type Specific Transduction Efficiency: The same MOI produces vastly different infection rates in different cell types due to receptor expression, cellular barriers, and innate immunity. A calculated MOI 10 might achieve 95% transduction in HEK293T but only 20% in primary T cells.
• Environmental Factors Not Modeled: Transduction efficiency depends on virus volume, incubation time, temperature, media composition, and enhancers (polybrene, spinoculation). These factors can shift effective MOI by 2-10 fold and aren't captured by simple MOI calculations.
Important Note: This calculator is designed for educational purposes to help understand MOI concepts and Poisson statistics in virology. For gene therapy research, viral vector production, or clinical applications, optimize MOI empirically for each cell type, validate titer using functional assays, and follow biosafety protocols. Consult institutional biosafety committees for work with viral vectors.
The MOI calculations and viral transduction principles referenced in this content are based on authoritative virology and molecular biology sources:
For bulk transductions, MOI 1-5 is typical, achieving 63-99% infection. For clonal selection where single integrations are desired, use MOI 0.1-0.3. Higher MOIs (>5) increase infection rate but also raise the risk of multiple integrations and cytotoxicity. Always optimize for your specific cell type and application. Understanding MOI selection helps you design experiments that achieve your goals while avoiding cytotoxicity and multiple integrations. The calculator shows Poisson predictions for different MOIs—use them to guide your selection.
Viral infection follows Poisson statistics, meaning particles distribute randomly among cells. At MOI 1, while on average each cell receives one particle, some cells receive zero while others receive two or more. The probability of a cell receiving zero particles at MOI 1 is e⁻¹ ≈ 36.8%. To infect >95% of cells, you need MOI ≥ 3. The fraction infected at least once follows: P(≥1) = 1 - e^(-MOI). Understanding Poisson statistics helps you see why MOI 1 doesn't infect all cells and why higher MOIs are needed for complete infection.
Use the formula: Volume (µL) = (MOI × Cell Count) / (Titer per mL) × 1000. For example, to achieve MOI 1 with 100,000 cells and a titer of 10⁸ TU/mL: Volume = (1 × 100,000) / (10⁸) × 1000 = 1 µL. The × 1000 factor converts from mL to µL. This calculator performs this calculation automatically when you select 'Volume from MOI' mode. Understanding this formula helps you determine how much virus stock to add to achieve your target MOI.
TU/mL (Transducing Units) measures functional, infectious particles and is commonly used for lentivirus. vg/mL (viral genomes) measures total viral DNA copies and is used for AAV. Physical particle counts (vg) are typically 10-1000× higher than infectious titers (TU) because not all particles are infectious. Use the titer that matches your experimental endpoint: use TU/mL for functional transduction, use vg/mL if measuring physical particles. Understanding this distinction helps you use the correct titer values in calculations and interpret experimental results correctly.
Adding more volume increases the amount of virus but also dilutes it in the culture medium. The key parameter is total infectious particles added, not volume alone. For small volumes, ensure accurate pipetting. For large volumes, consider that extended incubation in virus-containing medium may cause toxicity. Some protocols recommend concentrated virus in minimal volume. The formula shows that volume and titer are inversely related: higher titer requires less volume for the same MOI. Understanding this relationship helps you optimize transduction conditions.
Several factors affect actual transduction: cell type and receptor expression, cell health and cycle stage, virus pseudotype (VSV-G, native envelope, etc.), presence of transduction enhancers (polybrene, RetroNectin), virus quality and age (titers degrade over time), and incubation conditions (volume, time, temperature). Poisson predictions assume ideal conditions where all particles are equally infectious and all cells are equally susceptible. Always verify transduction efficiency with a reporter or marker. Understanding these factors helps you optimize conditions and interpret experimental results correctly.
Polybrene (hexadimethrine bromide) neutralizes charge repulsion between viral particles and cell membranes, often improving transduction 2-10×. Typical concentrations are 4-8 µg/mL. However, polybrene is toxic to some cell types (especially primary and stem cells). Test cytotoxicity first and consider alternatives like RetroNectin for sensitive cells. Understanding when to use polybrene helps you optimize transduction efficiency while avoiding cytotoxicity. The calculator provides theoretical predictions—polybrene may improve actual efficiency beyond these predictions.
Use low MOI (0.1-0.3) where most infected cells receive only one viral particle. At MOI 0.1, ~90% of cells are uninfected, ~9% receive one particle, and <1% receive multiple. After transduction, select for transduced cells (e.g., with antibiotic resistance or fluorescence), then expand clones and verify integration copy number by qPCR or Southern blot. Understanding low MOI helps you minimize multiple integrations, which is important for gene therapy and research applications where single-copy integration is desired.
Store lentivirus at -80°C in small aliquots to avoid repeated freeze-thaw cycles. Each cycle can reduce titer by 10-50%. For short-term storage (days), 4°C is acceptable but expect gradual loss. AAV is more stable and can tolerate more handling. Always aliquot fresh virus before initial freeze and record the date of preparation. Understanding storage requirements helps you maintain virus titer, which is critical for accurate MOI calculations. Degraded titer leads to lower actual MOI than calculated.
Yes, the Poisson model applies to any system where particles randomly encounter targets. For bacteriophage, MOI is typically much higher (10-100+) for complete lysis. For bacterial pathogens infecting mammalian cells, MOI varies widely by pathogen (1-1000). The core formula and statistics remain the same regardless of the infectious agent: MOI = Particles / Cells, and infection follows Poisson distribution. Understanding this universality helps you see that MOI concepts apply broadly across virology, microbiology, and biotechnology.
Calculate cells needed and volumes for well plates, flasks, and dishes before transduction.
Plan serial dilutions for virus titration and plaque assays.
Calculate volumes for polybrene, media supplements, and transduction reagents.
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