AI, machine learning, digitalization, automation, and computational biology – all are needed for synthetic biology to succeed at speed and scale. Synthetic biology experiments are so data-rich that it’s no longer effective to sift the data and find trends by hand. Nor is it efficient to hand-run experiments when robots can be quicker, more accurate, and work at all hours, with no caffeine needed.
Digital biology has made such leaps that we can now predict the structure of every known protein, visualize biology in VR, share experiment protocols between robots across the world, and run entire labs through the cloud. However, digital bio still has a long way to go, from standardization to implementation, cost, and accuracy. What new opportunities are emerging for innovation and discovery?
Applications:
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