# Synthetic associative learning in engineered multicellular consortia

## TL;DR

This paper introduces three innovative microbial consortia circuit designs that enable associative learning with memory, simplifying implementation compared to single-cell circuits, and offering potential applications in microbiome engineering and synthetic organoids.

## Contribution

It presents three novel two-cell microbial consortia circuits capable of associative learning and memory, reducing complexity in synthetic biological designs.

## Key findings

- Consortia circuits successfully demonstrate associative learning responses.
- The designs support both long-term and short-term memory.
- Potential applications include gut microbiome and organoid engineering.

## Abstract

Associative learning is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was early recognized to be a general trait of complex multicellular organisms but also found in "simpler" ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display associative learning responses to two classes of stimuli and displaying long and short-term memory (i. e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.