From a box of Cracker Jack to the Da Vinci Code, everyone enjoys deciphering secret messages. But biomedical engineers at Duke University have taken the decoding ring to pose where it has never been before—the patterns created by bacterial colonies.
Depending on the initial conditions used, such as nutrient levels and space limitations, bacteria tend to grow in specific ways. The researchers created a virtual bacterial colony and then controlled the growth conditions and the number and sizes of simulated bacterial dots to create an entire alphabet based on what the colonies would look like once they filled a virtual petri dish. They call this encoding scheme emorfi.
The encoding is not one-to-one because the final simulated pattern corresponding to each letter is not exactly the same each time. However, the researchers found that a machine learning program could learn to distinguish between them to recognize the intended letter.
“A friend can see many images of me over time, but none of them will be exactly the same,” explained Lingchong Yu, a professor of biomedical engineering at Duke. “But if all the images consistently highlight how I look overall, the friend will be able to recognize me even if he’s shown a picture of me he’s never seen before.”
To encrypt real messages, the encoder ultimately creates a movie of a series of patterns, each corresponding to a different letter. Although they may look similar to the untrained eye, a computer algorithm can tell them apart. As long as the recipient knows the set of initial conditions that led to their creation, an attacker shouldn’t be able to crack the code without their own powerful AI.
Try the cipher yourself. You can enter anything from your name to the Gettysburg address or even the Christmas classic “Don’t forget to drink your Ovaltine.”
Fractal patterns in growing bacterial colonies
Jia Lu et al, Distributed encoding and decoding of information using self-organized spatial patterns, Patterns (2022). DOI: 10.1016/j.pattern.2022.100590
Quote: AI Message Decoder Based on Bacterial Growth Patterns (2022, September 23), Retrieved September 25, 2022, from
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