Distributed Computing with Engineered Bacteria and Its Application in Solving Chemically Generated 2 × 2 Maze Problems

Abstract

This work presented an application of genetic distributed computing, where an abstract computational problem was mapped on a complex truth table and solved using simple genetic circuits distributed among various cell populations. Maze generating and solving are challenging problems in mathematics and computing. Here, we mapped all the input-output matrices of a 2 × 2 mathematical maze on a 4-input-4-output truth table . The logic values of four chemical inputs determined the 16 different 2 × 2 maze problems on a chemical space. We created six multiinput synthetic genetic AND gates, which distributed among six cell populations and organized in a single layer. Those cell populations in a mixed culture worked as a computational solver, which solved the chemically generated maze problems by expressing or not expressing four different fluorescent proteins. The three available "solutions" were visualized by glowing bacteria, and for the 13 "no solution" cases, no bacteria glowed. Thus, our system not only solved the maze problems but also showed the number of solvable and unsolvable problems. This work may have significance in cellular computation and synthetic biology.

Deep Learning Concepts and Applications for Synthetic Biology

“…In the study by Sarkar et al, 99 simple genetic circuits distributed among various bacterial populations were used to solve chemically generated 2 × 2 maze problems by selectively expressing four different fluorescent proteins, demonstrating the possibility of using engineered bacteria to perform distributed cellular computations and optimizations. In van der Linden et al, 100 the authors genetically implemented a perceptron capable of binary classification.…”

Adaptive Integration Algorithm for Distributed System Based on Particle Swarm Optimization

“…The data format is sensing information + node IP, and shares it with the central node connected to itself [16][17]. After the edge node directly shares the perception information to the central node, the central node will manage the information in a unified manner [18]. After receiving the multicast information from the central node, the edge node firstly identifies the information to identify whether it is the information shared by the central node., the specific process is shown in Figure 2.…”

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

“… 16–18 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, 19–21 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 27–30 were layered analogously to the electronic circuit design to create integrated genetic logic circuits and devices.…”