A single layer artificial neural network type architecture with molecular engineered bacteria for complex conventional and reversible computing

Abstract

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created molecular-devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such molecular devices, we established a set of rules by corelating truth tables, mathematical equations of ANN, and molecular-device design, which unlike molecular computing, does not require circuit diagram and the equation directly correlates the design of the molecular-device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in new platform for biomolecular computing, reversible computing and in transforming living cells as ANN-enabled hardware.