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Distributed Classifier Based on Genetically Engineered Bacterial Cell Cultures
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DOI：10.1021/sb500235p.png)
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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 toward 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 reshaping 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 reshaping 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
Keyword:
chemical pattern recognition
consensus classification
distributed sensing
machine learning
microbial population engineering
synthetic circuits
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