Synthetic neural-like computing in microbial consortia for pattern recognition

Ximing Li,Valeriia Kravchik,Maria Khoury,Netanel Korin,Ramez Daniel,Luna Rizik

doi:10.1038/s41467-021-23336-0

Ximing Li, Valeriia Kravchik + Show 4 more

Open Access

https://doi.org/10.1038/s41467-021-23336-0

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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.

Highlights

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks
The computational properties of biological systems can emerge from coordinative and collective interactions among basic components[2,3]. These components can be neurons interacting with other cells in the brain, bacteria communicating with other members in a community, or receptors participating in signaling pathways[4]
Analog design and control design handle a range of continuous input levels, focusing on system stability and design dynamics

Summary

Introduction

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. In contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Such approaches cannot be adapted for multiple tasks in biological contexts. Multi-cellular systems naturally allow distributed and parallel computing Using these features, studies have successfully implemented edge detection[17] and spatial pattern formation[18,19]. Inspired by biological neural networks, artificial neural networks (ANNs) are adaptive computing models that are commonly adopted to solve a wide range of tasks[23]. The connecting strengths between units, namely the weights, can be trained to achieve specific tasks This trainable feature allows ANNs to “learn” the weight values, so that tasks involving decision-making, such as pattern recognition, can be learned. A perceptron unit can be used for pattern recognition

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