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The new method uses engineered bacteria and AI to sense and record


A New Method Uses Engineered Bacteria and AI to Sense and Record Environmental Signals

A New Method Uses Engineered Bacteria and AI to Sense and Record Environmental Signals

Columbia’s synthetic biologists first engineered bacterial swarm patterns to record the environment in a visible way, using deep learning to decode the patterns; Applications can range from monitoring environmental pollution to building living materials

New York, NY—May 9, 2023—Researcher at Biomedical engineering Professor Tal Danino’s laboratory were brainstorming a few years ago about how they could engineer and apply natural pattern-forming bacteria. There are many species of bacteria, such as Excellent proteus (P. mirabilis), which arrange themselves into defined patterns on a solid surface visible to the naked eye. These bacteria can sense several stimuli in nature and respond to these cues by “swarming”—the highly coordinated and rapid movements of the bacteria are supported by their flagella, long tail-like structures that cause whip-like movements to help propel them.

For inspiration, the Danino team at Columbian Engineeringwho has a lot of experience using synthetic biology methods to manipulate bacteria, discusses where else they can find similar patterns in nature and what they do. They noted how tree rings recorded the age of trees and climate history, and that sparked their idea to implement them P. mirabilis rings as a recording system. They were also interested in applying AI to characterize different features of bacterial colony patterns, an approach they realized could then be used to decode the engineered patterns.

“For us, this is an untapped opportunity to create natural recording systems for certain cues,” said Danino, a member of Columbia’s Data Science Institute (DSI).

In a the new study, published May 4 in Natural Chemical Biology, researchers work with P. mirabiliscommonly found in soil and water and occasionally in the human gut, where it is known by its spotty-looking colony pattern. When bacteria are grown on solid growth medium Petri dishes, they alternate between the bacterial growth phase, which makes dense circles visible, and the bacterial movement, called a “swarming” movement, which expands the colony outward. SAVE VIDEO HERE

The team engineered the bacteria by adding what synthetic biologists call “genetic circuits”—systems of genetic divisions, logically arranged to make the bacteria behave in a desired way. The engineered bacteria sensed the presence of the inputs the researchers selected — from temperature to sugar molecules to heavy metals like mercury and copper — and responded by changing their swarming ability, which markedly altered the output pattern.

Working with Andrew LainePercy K. and Professor Vida LW Hudson from Biomedical engineering and DSI members and JiGuoassistant professor of neurobiology (in psychiatry) at Columbia University Irving Medical Center the researchers then applied deep learning – a sophisticated AI technique – to decode the environmental patterns from these, the same way scientists look at the rings on a tree trunk to understand the history of its environment. They used models that could classify patterns holistically to predict, for example, the concentration of sugars in a sample, and models that could describe or “segment” edges in patterns to predict, for example, how many times the temperature changed as the colony grew. .

Advantages of working with P. mirabilis is that, compared to many typical patterns of engineered bacteria, the original P. mirabilis patterns are visible to the naked eye without expensive visualization technology and form on solid agar substrates that are durable and easy to work with. This property increases the potential for implementing systems as sensor readouts in a variety of settings. Using deep learning to interpret patterns can allow researchers to extract information about the concentration of input molecules even from complex patterns.

“Our goal was to develop this system as a low-cost detection and recording system for conditions such as pollutants and toxic compounds in the environment,” said Anjali Doshi, lead author of the study and a recent PhD graduate of Danino’s lab. “To our knowledge, this work is the first study in which a naturally pattern-forming bacterial species has been engineered by a synthetic biologist to modify its native swarming abilities and function as a sensor.”

Such work could help researchers better understand how native patterns form, and more than that, could contribute to other areas of biotechnology beyond the field of sensors. Being able to control bacteria as a group rather than as individuals, and control their movement and organization within a colony, could help researchers construct living materials on a larger scale, and aid the Danino lab’s parallel goal of engineering bacteria into therapeutic “smart” life, by enabling precise control. better than the behavior of bacteria in the body.

This work is a novel approach to constructing macro-scale bacterial recorders, extending the framework for engineering the behavior of emerging microbes. The team next plans to build on their system by engineering the bacteria to detect a wider range of pollutants and toxins and moving the system to safe “probiotic” bacteria. Ultimately, they aim to develop devices to implement recording systems outside of the lab.


About Study

Journal: Natural Chemical Biology

The study is titled “Engineered bacterial swarm patterns as a spatial record of environmental inputs.”

Authors are: Anjali Doshi 1 , Marian Shaw 1 , Ruxandra Tonea1 , Soonhee Moon 1 , Rosalia Minyety 1 , Anish Doshi 1 , Andrew Laine 1 , Jia Guo3,4 & Tal Danino 1,5,6
1 Department of Biomedical Engineering, Columbia University
2 Department of Electrical Engineering and Computer Science, University of California, Berkeley
3 Department of Psychiatry, Columbia University
4 Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University
5 Herbert Irving Comprehensive Cancer Center, Columbia University
6 Institute of Data Science, Columbia University

This work was supported by the NSF CAREER Award (1847356 to TD), the Blavatnik Fund for Innovations in Health (TD), and the NSF Graduate Research Fellowship (AD, Fellow ID 2018264757).

AD, MS, JG, AL and TD are named as inventors in a provisional patent application that Columbia University has filed with the US Patent and Trademark Office relating to all aspects of this work. The remaining authors declare no competing interests.



DOIs: 10.1038/s41589-023-01325-2



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