DOE Experiment Generator

Design of Experiments — Run Table Generator
Bioprocess Preset
Design Type
Factors (2-8 supported)
Name Low (-1) High (+1) Unit
Center Points
Randomize
Design Summary
Factor Levels Across Runs
Run Table
Design Matrix Heatmap

How to Use the DOE Experiment Generator

This free Design of Experiments (DOE) generator helps bioprocess engineers, process development scientists, and researchers create statistically rigorous experimental designs without expensive software like JMP or Minitab. Select a bioprocess preset or define your own factors, choose a design type, and generate a randomized run table ready for the lab.

The tool supports six design types: Full Factorial (2k) for complete characterization of 2-4 factors, Fractional Factorial (2k-p) for efficient screening of 4-8 factors, Plackett-Burman for main-effect screening of many factors in minimal runs, Central Composite Design (CCD) for response surface modeling, Box-Behnken for 3-level response surface designs that avoid extreme corners, and Definitive Screening Design (DSD) for simultaneous screening and response surface estimation in a single design.

Every run table includes a standard order column, coded factor levels (-1, 0, +1), actual values computed from your low/high settings, run type identification (factorial, center, or axial point), and a response column for recording results. Export to CSV for analysis in any statistics package, or print a run sheet for direct use at the bench.

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Frequently Asked Questions

Design of Experiments in Bioprocess Development

Design of Experiments is a cornerstone of Quality by Design (QbD) approaches mandated by ICH Q8 for pharmaceutical development. In bioprocess engineering, DOE is used at every stage: screening critical process parameters (CPPs) during early development, optimizing conditions during process characterization, and defining the design space for regulatory filings. A well-designed experiment can save weeks of bench time compared to one-factor-at-a-time approaches while providing far richer information about factor interactions and process robustness.

Common bioprocess DOE applications include CHO fed-batch optimization (pH, temperature, dissolved oxygen, feed rate, osmolality), E. coli expression screening (inducer concentration, induction temperature, induction OD, post-induction time), downstream purification optimization (loading density, flow rate, buffer pH, salt concentration), and media formulation studies. This tool generates the experimental design — for analysis of results, export the completed run table to R, Python (statsmodels), JMP, Minitab, or Design-Expert.

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