Abstract
Here, we adapted the basic concept of artificial neural networks (ANNs) 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 cellular 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 cellular devices, we established a set of rules by correlating truth tables, mathematical equations of ANNs, and cellular device design, which unlike cellular computing, does not require a circuit diagram and the equation directly correlates the design of the cellular device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in establishing a new platform for cellular computing, reversible computing and in transforming living cells as ANN-enabled hardware.
Highlights
An Arti cial Neural Network (ANN), partly inspired by the biological neurons in the brain, is a computing system where a set of nodes, called arti cial neurons is connected with appropriate mathematical equations within a network and is able to map complex nonlinear systems.[1]
We experimentally demonstrated that single-layer neural network type architectures that stemmed from those bactoneurons were general, exible and perform complex irreversible computation through a 2-to-4 decoder, a 4-to-2-priority encoder, a majority function, a 1-to-2 de-multiplexer, and a 2-to-1 multiplexer and reversible logic mapping through Feynman and Fredkin gates
We hypothesized that an abstract ANN model can be mapped into an engineered cellular model (Fig. 1a), where engineered cellular devices inside bacterial cell work as arti cial neuro-synapses
Summary
An Arti cial Neural Network (ANN), partly inspired by the biological neurons in the brain, is a computing system where a set of nodes, called arti cial neurons is connected with appropriate mathematical equations within a network and is able to map complex nonlinear systems.[1]. The advent of synthetic biology has allowed implementation of engineering principles in the molecular and cellular biology regime, where many genetically encoded cellular devices, called synthetic genetic circuits, have been created to carry out various computational operations.[16,17,18] Synthetic genetic circuits perform logical operations by engineered transcriptional and translational machinery Such systems may have applications in quantitative and mechanistic understanding of various natural cellular phenomena from the bottom-up,[19,20,21] programmed therapeutics,[22,23] biocomputation,[24,25] and smart living materials.[26] One of the major approaches in synthetic biology is adapting electronic circuit principles to create complex computing functions, where synthetic genetic logic gates[27,28,29,30] were layered analogously to the electronic circuit design to create integrated genetic logic circuits and devices.[31,32,33,34,35] Electronic analogous devices have been created in bacterial and mammalian cells. These circuits were either realized in a single cell[27,28,29,32] or distributed among multiple cells.[30,33,34,35] Such system development remains difficult, is not properly scalable and is not streamlined.[18,30,36]
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