---
title: "Introducing DREAM-bio: exploring whole bioreactor design spaces"
description: DREAM-bio is p4b's digital bioreactor design and optimization service. Together with you we define the parameters to vary, then the simulations are generated and run semi-automatically and you get structured, comparable results across the whole design space.
image: https://www.p4b.io/hubfs/website/video/dream-optimization.webp
---

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DREAM-bio · 23 Oct 2026

# Introducing DREAM-bio: exploring whole bioreactor design spaces

DREAM-bio is p4b's digital bioreactor design and optimization service. Together with you we define the parameters to vary, then the simulations are generated and run semi-automatically and you get structured, comparable results across the whole design space.

Exploring a design space with different geometries, operating conditions and configurations can easily mean a few hundred simulations. Done by hand, that usually takes weeks: each setup needs manual changes, each run needs validation, and each result needs sorting and comparison. DREAM-bio makes the design space itself the input.

With DREAM-bio, we work with you to define the relevant parameters and degrees of freedom. The simulations are then generated and run semi-automatically as a service. You receive structured, comparable results for better decisions, both for new designs and for existing equipment. DREAM-bio is developed with M-Star CFD.

Bioreactor performance is shaped by geometric choices, and DREAM-bio lets you explore them systematically instead of committing early to a single configuration. You decide what to vary: the position, number, type and shape of the stirring elements, the vessel geometry, the size and geometry of the baffles, and more.

Exploring a design space is only useful if meaningful parameters are evaluated. DREAM-bio provides a structured set of CFD evaluations, including mixing behavior, shear forces, mass transfer and gassing, particle suspension, mixing dead zones and the risk of cell starvation. Because every simulated configuration is evaluated the same way, trends, trade-offs and limiting factors become visible.

Averaged values are sometimes not enough, because cells experience local conditions along their lifelines. Lifeline analysis shows what cells encounter as they move through the bioreactor: exposure to shear stress, fluctuations in dissolved oxygen and nutrients, residence times in low-mixing regions, and how often and how long critical events occur. One example shows glucose and oxygen availability for individual cells in a 15,000 L bioreactor.

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**Lena Achleitner**Senior Bioprocess Engineer, Key Account Manager[lena.achleitner@p4b.io](mailto:lena.achleitner@p4b.io)

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