Synthetic neural-like computing in microbial consortia for pattern recognition

Ximing Li, Luna Rizik, Valeriia Kravchik, Maria Khoury, Netanel Korin and Ramez Daniel ()
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Ximing Li: Department of Biomedical Engineering Technion—Israel Institute of Technology, Technion City
Luna Rizik: Department of Biomedical Engineering Technion—Israel Institute of Technology, Technion City
Valeriia Kravchik: Department of Biomedical Engineering Technion—Israel Institute of Technology, Technion City
Maria Khoury: Department of Biomedical Engineering Technion—Israel Institute of Technology, Technion City
Netanel Korin: Department of Biomedical Engineering Technion—Israel Institute of Technology, Technion City
Ramez Daniel: Department of Biomedical Engineering Technion—Israel Institute of Technology, Technion City

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

Date: 2021
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DOI: 10.1038/s41467-021-23336-0

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