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Modern rapid-prototyping technologies for life sciences, such as printable and digital microfluidics allow for significantly more flexibility in experimental design while scaling experiments to harvest exponentially more data compared with traditional processes. This leap in innovation poses new questions for how to deal with the information produced in labs, and further triggers new opportunities for applications in pharma and healthcare.
Open-sourcing the experimental design - the hardware, software and wetware - as well as the resulting data seems logical for analysing large datasets. Pooling of open knowledge allows for the development of new types of business models in the pharmaceutical field based on big data of for example clinical diagnostic data on the one hand, but also to personalize treatments for patients in hospitals. New healthcare problems can be tackled using this approach. A prominent example is the antibiotic resistance threat, which could in future be countered with automated and personalized phage therapy at the point of care.
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