Distributed classifier based on genetically engineered bacterial cell cultures

TL;DR

This paper proposes a novel distributed classifier using genetically engineered bacterial cell populations with diverse synthetic biosensor circuits, trained through population reshaping techniques like FACS.

Contribution

It introduces a new approach to create complex classifiers from simple microbial cells with randomized sensitivities, trained via population selection methods.

Findings

Feasibility demonstrated through computational modeling

Synthetic gene circuits with randomized control sequences created

Population reshaping enables classification of input patterns

Abstract

We describe a conceptual design of a distributed classifier formed by a population of genetically engineered microbial cells. The central idea is to create a complex classifier from a population of weak or simple classifiers. We create a master population of cells with randomized synthetic biosensor circuits that have a broad range of sensitivities towards chemical signals of interest that form the input vectors subject to classification. The randomized sensitivities are achieved by constructing a library of synthetic gene circuits with randomized control sequences (e.g. ribosome-binding sites) in the front element. The training procedure consists in re-shaping of the master population in such a way that it collectively responds to the "positive" patterns of input signals by producing above-threshold output (e.g. fluorescent signal), and below-threshold output in case of the "negative" patterns. The population re-shaping is achieved by presenting sequential examples and pruning the population using either graded selection/counterselection or by fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of experimental implementation of such system computationally using a realistic model of the synthetic sensing gene circuits.