CRISPulator

CRISPulator simulates pooled CRISPR genetic screens to predict how experimental parameters affect screen robustness and to inform optimal screen design.

Key Features:

• Monte Carlo simulation: Uses Monte Carlo simulation to model variability and outcomes across parameter combinations.
• CRISPR modality support: Models both CRISPR interference (CRISPRi) and CRISPR nuclease (CRISPRn) screening modalities.
• Growth and survival modeling: Simulates screens based on cell growth or survival metrics to evaluate selection-based outcomes.
• FACS-based phenotype modeling: Simulates fluorescence-activated cell sorting experiments to model fluorescent reporter phenotypes.
• In silico parameter exploration: Explores a wide parameter space in silico to assess impacts on robustness, cost, and scalability.
• Design-rule derivation: Identifies non-obvious rules for optimal screen parameter selection based on simulation results.
• Experimental trial reduction: Predicts parameter impacts to reduce the need for resource-intensive experimental trials.

Scientific Applications:

• Pooled genetic screen optimization: Inform design choices for pooled CRISPR screens to improve robustness and efficiency.
• CRISPRi and CRISPRn experiment planning: Compare expected outcomes across CRISPRi and CRISPRn modalities under varying parameters.
• Selection-based screen assessment: Predict performance of growth- or survival-based selection screens.
• Reporter-based FACS screen design: Evaluate experimental parameters for fluorescence-activated cell sorting of reporter phenotypes.
• Cost and scalability evaluation: Assess how parameter choices influence experimental cost and scalability in silico.

Methodology:

Monte Carlo simulation to predict the impact of experimental parameters on the robustness of pooled CRISPR screens.