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Engineered Bacteria Convert a 3-Bit Binary Code to a 3-Bit Gray Code by Multicellular Artificial-Neural-Network-Type

Saswata Chakraborty^1,2, Sangram Bagh^1,2

¹Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Block AF, Sector-I, Bidhannagar, Kolkata 700064, India.

ACS Synthetic Biology

|May 7, 2025

Summary

Genetically engineered E. coli cells form a novel artificial neural network (ANN) capable of binary to Gray code conversion. This synthetic biology approach advances biocomputer technology and bacterial ANNs.

Keywords:

Artificial Neural Network Biocomputer Cellular Device Code Converter Synthetic Biology

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Area of Science:

Synthetic Biology
Biocomputing
Artificial Neural Networks

Background:

Neuromorphic computing using engineered cells is emerging, with potential for complex problem-solving.
Expanding computational functions is key to advancing cell-based computing.
Binary to Gray code conversion is a foundational digital electronics concept.

Purpose of the Study:

To create a single-layer artificial neural network (ANN) using genetically engineered E. coli.
To demonstrate the ANN's function as a 3-bit binary to Gray code converter.

Main Methods:

Utilized genetically engineered E. coli populations in liquid culture.
Implemented a chemical input system representing binary code (1/0).
Detected converted Gray codes via expression of three fluorescent proteins.

Main Results:

Successfully engineered E. coli to perform a 3-bit binary to Gray code conversion.
Established a functional ANN architecture within bacterial populations.
Demonstrated the feasibility of chemical inputs and fluorescent outputs for computation.

Conclusions:

This work presents a novel biocomputer capable of fundamental code conversion.
Highlights the potential of bacterial ANNs and synthetic biology in biocomputer development.
Paves the way for more complex computational functions using engineered cells.