Multicellular artificial neural network-type architectures demonstrate computational problem solving.
Journal:
Nature chemical biology
PMID:
39285005
Abstract
Here, we report a modular multicellular system created by mixing and matching discrete engineered bacterial cells. This system can be designed to solve multiple computational decision problems. The modular system is based on a set of engineered bacteria that are modeled as an 'artificial neurosynapse' that, in a coculture, formed a single-layer artificial neural network-type architecture that can perform computational tasks. As a demonstration, we constructed devices that function as a full subtractor and a full adder. The system is also capable of solving problems such as determining if a number between 0 and 9 is a prime number and if a letter between A and L is a vowel. Finally, we built a system that determines the maximum number of pieces of a pie that can be made for a given number of straight cuts. This work may have importance in biocomputer technology development and multicellular synthetic biology.