A Logically Reversible Double Feynman Gate with Molecular Engineered Bacteria Arranged in an Artificial Neural Network-Type Architecture

Reversible logic gates are the key components of reversible computing that map inputs and outputs in a certain one-to-one pattern so that the output signals can reveal the pattern of the input signals. One of the main research foci of reversible computing is the implementation of basic reversible gates by various modalities. Though true thermodynamic reversibility cannot be attained within living cells, the high energy efficiency of biological reactions inspires the implementation of reversible computation in living cells. The implementation of synthetic genetic circuits is mostly based on conventional irreversible computing, and the implementation of logical reversibility in living cells is rare. Here, we constructed a 3-input-3-output synthetic genetic reversible double Feynman logic gate with a population of genetically engineered E. coli cells. Instead of following hierarchical electronic design principles, we adapted the concept of artificial neural networks (ANN) and built a single-layer artificial network-type architecture with five different engineered bacteria, named bactoneurons. We used three extracellular chemicals as input signals and the expression of three fluorescence proteins as the output signals. The cellular devices, which combine the input chemical signals linearly and pass them through a nonlinear activation function and represent specific bactoneurons, were built by designing and creating small synthetic genetic networks inside E. coli. The weights of each of the inputs and biases of individual bactoneurons in the bacterial ANN were adjusted by optimizing the synthetic genetic networks. When arranging the five bactoneurons through an ANN-type architecture, the system generated a double Feynman gate function at the population level. To our knowledge, this is the first reversible double Feynman gate realization with living cells. This work may have significance in development of biocomputer technology, reversible computation, ANN wetware, and synthetic biology.