ArticleACS synthetic biology2015
Distributed classifier based on genetically engineered bacterial cell cultures.
Article in ACS synthetic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- An Analysis of the Governance, Ethical, Legal, and Social Implications of Biocomputing.Science and engineering ethics · 2026Article
- A synthetic protein-level neural network in mammalian cells.Science (New York, N.Y.) · 2024Article
- Connectome-based reservoir computing with the conn2res toolbox.Nature communications · 2024Article
- Synthetic neural-like computing in microbial consortia for pattern recognition.Nature communications · 2021Article
- From Microbial Communities to Distributed Computing Systems.Frontiers in bioengineering and biotechnology · 2020Review
- Synthetic Gene Circuits Learn to Classify.Cell systems · 2017Article
- Synthetic biology routes to bio-artificial intelligence.Essays in biochemistry · 2016Review
- Nano-guided cell networks as conveyors of molecular communication.Nature communications · 2015Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.