1 research outputs found
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