Understanding Design of Experiments (DoE) in Protein Purification

This webcast, from GE Life Sciences, seeks to provide an understanding Design of Experiments (DoE) using an example of protein purification. It begins with a good overview of the reason why multi-factorial experiments must be used while changing multiple factors at the same time in order to see interactions between factors. These interactions are completely missed by one-factor-at-a-time experiments.

While it is a good introduction it might be a bit confusing if you are not familiar with multi-factorial designed experiments. You may want to read some of the links below or take advantage of the ability to pause the video to think about what he says or to replay portions you don’t pick up immediately.

I have discussed the value of design of experiments in multiple posts on this blog in the past, including: Introductory Videos on Using Design of Experiments to Improve Results by Stu Hunter, Design of Experiments: The Process of Discovery is Iterative and Factorial Designed Experiment Aim.

He also provides a good overview of 3 basic aims of multivariate experiment (DoE):

  • screening (to determine which factors have the largest impact on the results that are most important)
  • optimization (optimize the results)
  • robustness testing (determine if there are risks in variations to factors)

Normally an experiment will focus on one of these aims. So you don’t know the most important factors you may choose to do a screening experiment to figure out which factors you want to study in detail in an optimization experiment.

It could be an optimized set of values for factors provides very good results but is not robust. If you don’t have easy way to make sure the factors do not vary it may be worthwhile to choose another option that provides nearly as good results but is much more robust (good results even with more variation within the values of the factors).

Related: YouTube Uses Multivariate Experiment To Improve Sign-ups 15% (2009)Combinatorial Testing for Software (2009)Marketers Are Embracing Statistical Design of Experiments (2005)

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