DNA and RNA have been compared to “instruction manuals” containing the information needed for living “machines” to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables — a task that is daunting to even the most intrepid human brains.
Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based “toehold” sequences and predict which ones will be most effective at sensing and responding to a desired target sequence. As reported in two papers published concurrently
Certain changes in a person’s heart and breathing rates could precede symptoms of COVID-19, an increasing number of studies suggests.
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Purdue University researchers are helping to develop physIQ software that could indicate that a person should get tested for COVID-19 by detecting specific changes in heart and breathing rates while the person wears a smartwatch. Pictured: Jennifer Anderson, Ph.D. student, Purdue’s Weldon School of Biomedical Engineering. (Purdue University photo/John Underwood)
Purdue University researchers have begun a study that would help determine if continuously collected biometric smartwatch data could be used to reliably and accurately detect these signs early, which could indicate that a potentially asymptomatic user should get tested for COVID-19.
Data from the study will inform new algorithms to be developed by physIQ, a Purdue-affiliated digital health technology company based in Chicago. The company has support from the
If you’ve eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine—both products that are “grown” in the lab—then you’ve benefited from synthetic biology. It’s a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach.
Now scientists at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding