Computing has long been confined to the rigid architecture of silicon chips, which rely on predictable logic and predefined instructions. But nature doesn’t work that way. Microbes respond to environmental shifts through networks of internal signals, functioning like decentralized systems that share and react to information. This behavior, once only of interest to biologists, is now drawing the attention of computer scientists and synthetic biologists.
Rice University’s research, led by biosciences professor Matthew Bennett, aims to explore how these naturally intelligent organisms can be repurposed as information processors. The project marks a shift from simply mimicking biology in software to directly embedding life into hardware.
Microbes as Processors
At the heart of this initiative is the idea that microbial systems can be treated like processors. According to Popular Mechanics, Bennett’s team is studying how bacteria transmit signals—both chemical and electrical—and how those signals might be decoded into usable information. The team’s objective is to engineer systems that don’t just perform tasks but learn from them, much like living organisms adapt through experience.
This involves investigating cellular memory, a microbial trait that allows cells to retain information about past stimuli. By understanding how microbes remember and react to their environments, the researchers hope to build networks capable of dynamic, self-modifying behavior. “Living computers may one day adapt and evolve in ways that surpass the capabilities of traditional machines,” Bennett said in a press release, as cited by the source.
What makes microbes especially promising is their existing communication infrastructure. Bacteria don’t operate in isolation—they form colonies, talk to one another, and even exchange information across species. This natural connectivity could be harnessed to build massively parallel systems capable of performing tasks traditional machines can’t match in flexibility or responsiveness.
Neurons on a Chip
Microbial computing isn’t the only biological route under exploration. Some startups have already begun integrating human cells into electronics. One such company, Cortical Labs, has developed what it calls Synthetic Biological Intelligence (SBI), a system based on human neurons embedded in silicon chips. These neurons form neural networks similar to those found in the brain and can respond to electrical stimulation.
Cortical Labs’ SBI system, known as CL-1, was able to play a version of Pong, a result that caught widespread attention. This living neural network even processed information faster than the silicon chips used in training AI language models.
Swiss startup Final Spark has also entered the space, creating Neuroplatform, a hybrid system that connects lab-grown brain organoids to electronic circuits. The organoids are stimulated with electric pulses, enabling them to interact with digital programs. While the ethical questions around such systems are still under debate, the performance benefits are already being noted.
A Responsive, Living Interface
Back at Rice, the vision isn’t just to build a new kind of computer—it’s to rethink what computers are. If microbes can serve as processors, then electronic interfaces could become tools for directing and enhancing their natural capabilities. This opens the possibility of systems that react to chemical signals in real time, potentially functioning as biosensors for pollutants, pathogens, or even neurological markers.
As reported by Popular Mechanics, such a system would allow for environmental responsiveness well beyond what current sensors can achieve. Instead of relying on programmed thresholds, these bio-computers could “decide” how to react based on learned conditions, adapting their behavior to changing inputs.
Bennett’s team is also examining the ethical and social implications of introducing living machines into public and commercial domains. The long-term applications remain speculative, but the foundation is being laid for computers that grow, learn, and evolve—just like life itself.