A step forward it seems on synthetic biology is to automate aspects of it.
Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You
Lawrence Berkeley National Laboratory
September 25, 2020
A tool developed by researchers at the Lawrence Berkeley National Laboratory adapts machines learning algorithms to the field of synthetic biology to predict how changes in a cell's DNA or biochemistry will impact its behavior, and to make recommendations for the next engineering cycle. The researchers used the Automated Recommendation Tool (ART) to guide the metabolic engineering process to increase production of the amino acid tryptophan. They selected five genes representing a total of almost 8,000 potential combinations of biological pathways; ART ultimately recommended a design that boosted tryptophan production by 106% over the state-of-the-art reference strain and 17% over the best designs used for training the model. Said Berkeley Lab's Hector Garcia Martin, "This is a clear demonstration that bioengineering led by machine learning is feasible, and disruptive if scalable."
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