English

Distributed classifier based on genetically engineered bacterial cell cultures

Molecular Networks 2014-05-22 v1 Neurons and Cognition

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.

Keywords

Cite

@article{arxiv.1405.5328,
  title  = {Distributed classifier based on genetically engineered bacterial cell cultures},
  author = {Andriy Didovyk and Oleg I. Kanakov and Mikhail V. Ivanchenko and Jeff Hasty and Ramón Huerta and Lev Tsimring},
  journal= {arXiv preprint arXiv:1405.5328},
  year   = {2014}
}

Comments

31 pages, 9 figures

R2 v1 2026-06-22T04:19:40.674Z