Biomolecular computing shows potential for next-generation integrated circuits

A new study has uncovered the remarkable potential of biomolecular computing that could provide innovative solutions in next generation of integrated circuits. In their article published in Materials Today Nano, the authors review existing “biological integrated circuits” that utilize DNA or proteins for clinical analysis tasks and also explore the prospects of their integration with classical integrated circuits.

Computational systems, and their core components—integrated circuits—have evolved by implementing computational functions inherent in biological systems. Currently, research in the field of integrated circuits stands at a crossroads.

On one hand, traditional technologies have reached their physical limits. On the other hand, bio-inspired computing is actively developing, utilizing either software methods (neuromorphic models) or new functional elements (memristors). In recent decades, researchers have managed to “harness” DNA for performing biocomputations in medical applications.

Lead author Ivan Bobrinetskiy states, “We have been combining biomolecules with, for example, field-effect transistors for a long time in the development of new electronic biosensors. An interesting question is whether we will be able to use such bioelectronic microchips to solve a wider range of problems.”

The main advantage of such a system is its highest parallelism. While a conventional processor executes commands sequentially, billions of molecules within a single droplet of liquid can work simultaneously, with each one solving a part of the overall task.

This opens the path to ultra-fast data analysis, for example, enabling the simultaneous diagnosis of multiple diseases from a single drop of blood or accelerating the performance of artificial intelligence algorithms in pattern recognition. Furthermore, such computations potentially require significantly less energy.

According to scientists’ estimates, the technology will be most useful for advancing personalized medicine, where rapid and accurate analysis of complex biological data is essential. It could also find applications in secure information storage and the development of new encryption systems based on biological principles.