DOE Experiment Generator

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, optimising conditions during process characterisation, 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 optimisation (pH, temperature, dissolved oxygen, feed rate, osmolality), E. coli expression screening (inducer concentration, induction temperature, induction OD, post-induction time), downstream purification optimisation (loading density, flow rate, buffer pH, salt concentration), and media formulation studies. This tool now handles the full pipeline from design to predicted optimum in your browser — you can still export the run table to external statistical software if your workflow prefers it.

How to Use the DOE Experiment Generator

This free Design of Experiments (DOE) generator is a complete platform — design, analyse, and identify the optimum in one tool, without paying for JMP, Minitab, or Design-Expert. The workflow follows the standard 6-step DOE process visible in the stepper at the top of the page:

1. Define objective — name the responses you'll measure (titre, viability, productivity, HCP, etc.) and choose a goal for each: maximise, minimise, or hit a target value. Up to four responses can be optimised simultaneously.
2. Choose factors & ranges — pick a bioprocess preset (CHO Fed-Batch, E. coli Expression, Pichia, Chromatography, Cell Culture Media, UF/DF, AAV Production) or define your own 2–8 factors with low/high levels and units.
3. Design experiment — pick one of six design types. Each tab shows the live run count for your current factor set so you can balance information against lab effort: Full Factorial (2^k), Fractional Factorial (2^k-p), Plackett-Burman, Central Composite (CCD), Box-Behnken, or Definitive Screening (DSD).
4. Run trials — export the randomised run sheet to CSV or print it. Record measured responses directly in the Run Table (cells are editable), paste a column from Excel, or click Demo data to see how the analysis works before you have real data.
5. Analyze interactions — fit an OLS regression model, see a plain-language summary, the Effects Pareto with significance threshold, an Effects Table with p-values and star ratings, residual diagnostics (residual-vs-fitted + Q-Q plot), and full ANOVA with lack-of-fit decomposition when replicates exist.
6. Identify optimum — see which actual run scored best, the analytically-solved per-response optima, the multi-response best compromise via Derringer-Suich desirability, and a contour plot of any two factors with the optimum marked. Add 3 confirmation runs at the predicted settings with a single click to validate the prediction in the lab.

Built-in design types: Full Factorial for complete characterisation of 2-4 factors, Fractional Factorial for efficient screening of 4-8 factors, Plackett-Burman for main-effect screening, Central Composite Design (CCD) for response surface modelling, Box-Behnken for 3-level designs that avoid extreme corners, and Definitive Screening Design (DSD) for combined screening + RSM in minimal runs.

All analysis runs client-side in your browser — no data leaves your machine. CSV export is still available at any stage so you can also feed the run table to R, Python (statsmodels), JMP, Minitab, or Design-Expert if you prefer.

Related Articles

Frequently Asked Questions

Resources

• NIST Engineering Statistics Handbook — Process Improvement (DOE)
• ResearchGate — Design of Experiments Topic
• ICH Q8(R2) — Pharmaceutical Development Guidelines