Machine Learning X Synthetic Biology: An Unprecedented Duo
Written by Rylan Donohoe, Artwork by Angela Zhu
“The possibilities are revolutionary. . . . If you’re able to create new cells to specification in a couple weeks or months instead of years, you could really revolutionize what you can do with bioengineering.”
As any synthetic biologist would likely affirm, bioengineering is a slow process. It takes months, if not years, to design and redesign biological systems that possess new desired qualities, at least for now…
Machine learning is a branch of artificial intelligence that exploits a computer’s ability to learn more and more from massive amounts of data. Since gaining spotlight in recent years, machine learning has revolutionized a number of software applications—it is about time we see what it can bring to the field of biology.
That is precisely what Dr. Tijana Radivojević and her team set out to do. These scientists, from the Lawrence Berkeley National Laboratory, developed a novel tool—the Automated Recommendation Tool (ART)—that leverages machine learning algorithms to predict the impacts of altering the DNA or biochemistry of a cell. Rather than guess and check, machine learning offers us a systematic method of engineering biological systems, both improving and quickening the current process.
Other synthetic biologists around the world have similarly harnessed the power of machine learning for their own projects. Nostos Genomics, a Berlin-based startup founded by Dr. Rocío Acuña Hidalgo and David Gorgon, designed an artificial intelligence system with the ability to identify gene variations linked to more than 10,000 diseases. AION, as it is named, has the potential to significantly reduce diagnosis times in hospitals worldwide.
Machine learning has clearly catalyzed a major turning point in synthetic biology. As Dr. Hector Garcia Martin of the ART project says, “The possibilities are revolutionary. . . . If you’re able to create new cells to specification in a couple weeks or months instead of years, you could really revolutionize what you can do with bioengineering.”
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