“Basically we were trying to find out from the mathematical model if we could build a living microbiome on a nonliving host and control the host through the microbiome,” said Warren Ruder, an assistant professor of biological systems engineering in both the College of Agriculture and Life Sciences and the College of Engineering.
“We found that robots may indeed be able to function with a bacterial brain,” he said.
For future experiments, Ruder is building real-world robots that will have the ability to read bacterial gene expression levels in E. coli using miniature fluorescent microscopes. The robots will respond to bacteria he will engineer in his lab.
On a broad scale, understanding the biochemical sensing between organisms could have far reaching implications in ecology, biology, and robotics.
Living Cells Interfaced with a Biomimetic Robot as a Model System for Host-Microbiome Interactions. (A) A synthetic gene network – also known as an engineered gene circuit. Uploading a gene circuit into living bacteria endows cells with a programmable biomolecular network. (B) Engineered bacteria and their circuits can be introduced into an organism’s microbiome. The networks of the host and microbiome combine to form a complete gene network. In the absence of the complete host-microbiome network, host behavior is erratic. A programmed microbiome drives new, and potentially rational, host behavior. (C) A robot with a microfluidic chemostat mimics the microbiome’s environment within an organism. The robot is conceptualized to include a miniature fluorescent microscope, along with the pumps necessary to deliver inducers to the onboard microfluidic chemostat. This microscope allows for modulations in the reporter protein levels to be interpreted by the robot electronically. In the absence of a living microbiome, robotic host behavior can be erratic. A programmed, living microbiome drives new host robotic behavior.
In agriculture, bacteria-robot model systems could enable robust studies that explore the interactions between soil bacteria and livestock. In healthcare, further understanding of bacteria’s role in controlling gut physiology could lead to bacteria-based prescriptions to treat mental and physical illnesses. Ruder also envisions droids that could execute tasks such as deploying bacteria to remediate oil spills.
The findings also add to the ever-growing body of research about bacteria in the human body that are thought to regulate health and mood, and especially the theory that bacteria also affect behavior.
The study was inspired by real-world experiments where the mating behavior of fruit flies was manipulated using bacteria, as well as mice that exhibited signs of lower stress when implanted with probiotics.
Ruder’s approach revealed unique decision-making behavior by a bacteria-robot system by coupling and computationally simulating widely accepted equations that describe three distinct elements: engineered gene circuits in E. coli, microfluid bioreactors, and robot movement.
The bacteria in the mathematical experiment exhibited their genetic circuitry by either turning green or red, according to what they ate. In the mathematical model, the theoretical robot was equipped with sensors and a miniature microscope to measure the color of bacteria telling it where and how fast to go depending upon the pigment and intensity of color.
The model also revealed higher order functions in a surprising way. In one instance, as the bacteria were directing the robot toward more food, the robot paused before quickly making its final approach — a classic predatory behavior of higher order animals that stalk prey.
Ruder’s modeling study also demonstrates that these sorts of biosynthetic experiments could be done in the future with a minimal amount of funds, opening up the field to a much larger pool of researchers.
The microbiome’s underlying dynamics play an important role in regulating the behavior and health of its host. In order to explore the details of these interactions, we created an in silico model of a living microbiome, engineered with synthetic biology, that interfaces with a biomimetic, robotic host. By analytically modeling and computationally simulating engineered gene networks in these commensal communities, we reproduced complex behaviors in the host. We observed that robot movements depended upon programmed biochemical network dynamics within the microbiome. These results illustrate the model’s potential utility as a tool for exploring inter-kingdom ecological relationships. These systems could impact fields ranging from synthetic biology and ecology to biophysics and medicine.
SOURCES – Virginia Tech, Scientific Reports