Visit our bioprocess monitoring and control web page to learn how to monitor bioprocess parameters, which parameters are relevant, and how to control process parameters using feedback loops.

The critical process parameters and setpoints will depend on the type of organism you cultivate. Visit our website for more detailed information about cell culture in bioreactors , stem cell bioprocessing , bacterial fermentation , and yeast fermentation .

A good way to speed up bioprocess development is the use of parallel bioprocess systems like the DASbox Mini Bioreactor System or the DASGIP Parallel Bioreactor System . They facilitate multiple experiments to be run simultaneously and like this saving time and resources. Are you interested in an example? Then have a look at this application note from the Zuerich University for Applied Sciences . The researchers used a DASbox Mini Bioreactor System to run four CHO cell culture runs in parallel and reached highly reproducible results.

The use of parallel bioprocessing enables a Design of Experiments (DoE) approach, which aims to uncover the complex relationships between process parameters in bioprocess development, through altering a number of variables simultaneously. Not only does this provide fundamental details on a variety of parameters, it also analyzes their potential interactions, and identifies which parameters have the largest effects on the final outcome. Combining DoE and parallel bioprocessing provides a powerful and efficient way to optimize cell cultivation using a minimal number of experiments.

Find out more about the DoE approach in our article:

For a great example of systematic optimization of process parameters in bioprocess development, we recommend you take a look at this application note from a team at the Medical University of Hannover . It describes how systematic optimization of process parameters, including pH, DO and feeding strategy, led to a more than 10-fold increase in cell density compared to uncontrolled conditions.

A wide variety of sensors and analyzers generate a vast amount of bioprocess data. The challenge is to efficiently analyze the data and turn it into insights. AI-based tools can help increase efficiency in bioprocess development by reducing the experimental effort per project, reducing time and cost, and increasing development capacity. They help increase the effectiveness of bioprocess development by providing better insights and process knowledge, enabling higher productivity and the development of more robust processes. For further reading, we recommend the resources of DataHow , a company at the forefront of applying a new generation of AI technologies to bioprocessing and one of our partners. Find information on how AI-based tools can reduce wet-lab experiments , what is model-based upstream process optimization , and more.