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When planning your DOE and thinking about which factors to investigate, and how to change them, there's a line to toe. To avoid spending too much effort re-learning things that are already known, you'll need to use your pre-existing knowledge. But at the same time, familiarity shouldn't breed complacency. It’s all too easy to develop experiments that confirm, rather than test, hypotheses. Not having a well-developed and robust theoretical framework for your experiment will prevent you from getting to grips with the complexity of your system. Let's say you want to optimize the expression of a target protein in bacterial cell culture. If you know, for instance, which growth media achieve high yields when you’re trying to optimize protein production with DOE, usually there's no need to confirm this experimentally. Formulae for many cell growth media, on the other hand, have been handed down and used unquestioningly by scientists for generations. Why would you risk taking something out if your cells might not grow properly? Because calculated risks are part of science. Cell growth is complex and there’s no perfect medium that gives excellent results in every possible case. It’s likely that many ingredients aren’t necessary for specific applications or may even be harmful: High levels of zinc may inhibit the growth of certain bacteria, for example. Investigating the composition of such apparently standard parts of the workflow can be useful: Some “unnecessary” components of the media can be very expensive, while others are actively harmful for the specific application. The moral of the story? Be open-minded. DOE gives you the tools to investigate your system in an unbiased way, which often reveals new insights and generates novel hypotheses. For best results, assume you don't know everything. #DOE #DesignOfExperiments #DOEForBiology

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