Cells Engineered to Compute Like Tiny Processors
Researchers at Hebrew University have engineered human cells to process multiple biological signals simultaneously, much like small computer chips. This breakthrough could lead to cells diagnosing diseases and responding automatically inside the body. The method uses RNA trans-splicing to join genetic messages together, potentially improving genetic computation efficiency.
Key points
- Hebrew University researchers engineered human cells to process multiple biological signals simultaneously, like small computer chips.
- PhD student Keren Roas and Dr. Lior Nissim developed an artificial genetic system using RNA trans-splicing and regulatory elements.
- The method could enable cells to diagnose diseases and respond automatically inside the body.
- The study, published in Nature Communications, describes a new approach to genetic computation that addresses traditional genetic circuit limitations.
- The researchers successfully built living versions of computer adders and multiplexers using the new method.
Researchers at Hebrew University have made a groundbreaking discovery in genetic computation by engineering human cells to process multiple biological signals simultaneously, much like small computer chips. This breakthrough could lead to cells diagnosing diseases and responding automatically inside the body.
The method uses RNA trans-splicing to join genetic messages together, potentially improving genetic computation efficiency. This approach addresses traditional genetic circuit limitations, where each additional instruction requires another layer of internal computation to operate correctly.
The researchers successfully built living versions of computer adders and multiplexers using the new method, demonstrating its potential for complex computational operations. This development could have significant implications for future medicine, enabling the design of new treatments and therapies.
The study, published in Nature Communications, highlights the potential of this new approach to genetic computation. Further research is needed to explore its full capabilities and applications.
Sources
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