This is part of an ongoing DoE series that I would have loved to read when I was first running these types of experiments in the lab. See Part II, a simple example of a DoE experiment in action, here.

In bioprocess dev, especially when working with bioreactors, it can be tempting to test one factor at a time: tweak the starting stir/air up a little, adjust the pH +/- 0.1, or increase the feed rate by 10% while the bugs are still growing exponentially. But in complex biological systems, variables rarely act in a vacuum. That’s where Design of Experiments (DoE) can come in.

What is DoE? Design of Experiments is a statistical approach to exploring how multiple variables influence a given experiment. Instead of changing one parameter while keeping others constant, DoE evaluates several factors simultaneously. The result is more insight, fewer runs, and (hopefully) better understanding of a system’s behavior. It can be, and hopefully is, an iterative process as well…meaning you can take learnings from one DoE (and its subsequent analysis) and continue on a path to optimization.

Why DoE Matters in Bioprocessing At its core, DoE empowers all scientists/engineers in the bioprocess space to:

• Identify critical process parameters (CPPs) that influence key outputs (e.g., biomass, titer, rate/yield)
• Understand how factors like pH, temperature, DO, and other variables interact
• Optimize conditions for things like yield, robustness, and other scale-up KPIs
• Reduce development time while improving reproducibility

Whether you’re optimizing upstream fermentation in a 2L benchtop reactor or scaling up to a much bigger system, DoE provides a rational framework to make informed decisions.

Types of Designs and When to Use Them DoE is not one-size-fits-all. Different designs serve different purposes:

For the purposes of explaining what is in my own wheel-house, I will likely just gloss over Box-Behnken and Placket-Burman Designs in this series.

• Full Factorial Designs: Ideal for 2-3 bioprocess parameters when full interaction understanding is needed (e.g., temp × pH × DO).
• Fractional Factorial Designs: Great for early-stage screening of multiple parameters like media composition, DO setpoint, or inoculum density.
• Plackett-Burman Designs: High-efficiency screening of 6+ variables when you’re unsure what’s most influential.
• Response Surface Methodology (RSM): Best for optimization once key factors are known. Useful for modeling nonlinear responses such as enzyme activity or metabolic shifts.
• Central Composite and Box-Behnken Designs: RSM workhorses for fine-tuning pH, temperature, or nutrient feeding to maximize output.

Common Pitfalls in Bioprocess DoE (and honestly most bioprocess experimentation–this might be the biggest thing to take away from all of this)

• Neglecting randomization: Run order should be randomized to avoid drift or run-to-run variation.
• Ignoring replication: Bioreactors are biological systems—variability is as promised as taxes. Replication helps you distinguish signal from noise.
• Overfitting: A model with too many terms for the number of runs may not generalize well to new conditions, or be useful at all.

Implementing DoE in the Lab Tools like JMP are widely used in fermentation R&D. These platforms let you:

• Design efficient experiments with constraints (e.g., changing feed rate, safe pH or DO limits)
• Use statistical tools like ANOVA and regression modeling to quantify variable significance and predict outcomes
• Visualize contour plots, 3D surfaces, and prediction profilers to guide optimization

From Flask to Pilot Plant DoE isn’t just about small-scale efficiency. It supports Quality by Design (QbD) initiatives and tech transfer by clearly defining possible next-steps for critical process variables. A well-executed DoE in a lab-scale bioreactor can save months in scale-up.

Conclusion: A *Culture* of Better Bioprocessing DoE is more than a statistical tool; it’s a mindset that encourages thoughtful design, data-driven decisions, and resource-efficient experimentation.

Keywords: Design of Experiments, DoE, bioprocess optimization, fermentation, upstream development, statistical analysis, RSM, bioreactor design, JMP.