Team:USP-UNESP-Brazil/Associative Memory/Introduction

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Associative Memory
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Objectives

The goal of this project is to build an associative memory network using E.coli populations that should be able to memorize a given pattern and reproduce it when exposed to a similar one. This is a demonstration of systemic memory storage in synthetic biology.

Background

Synthetic biology is a powerful tool for the construction of mechanisms capable of executing routines for processing and storing information in vivo. The final goal of this project is to build an associative memory network in a system of E.coli populations. In our design, a population of bacteria represents a “neuron” of the network. Each neuron communicates with the whole network through quorum sensing molecules (QSM), interacting in a repressive or excitatory way. Once all "neurons" are connected, a so called Hopfield associative memory architecture is defined. A similar idea was developed by Quian et al. [1] using DNA strand displacement cascades. They implemented a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembered four single-stranded DNA patterns and recalled the most similar one when presented with an incomplete pattern.

This interaction between "bacterial neurons" is based on a transcriptional regulation mechanism. The populations communicate by using quorum sensing substances and the transmitted information (inhibiting or exciting) will be defined by which transcriptional regulator the substance will promote. "Activating" a population stimulates its GFP production, while "repressing" the population inhibits it. The result is that from the moment the connections between neurons are defined, the system should return a predetermined response pattern, that should be observed due to GFP production.

Application

In addition to be one of the first Hopfield Network models made in vivo, this project shows different strains of bacteria communicating with each other, establishing balance and exhibiting systemic memory. This could be useful in the production of bioproducts, such as biofuels. For instance, a biosystem producing some compound inside a reactor could regulate itself according to specific parameter changes, such as temperature or nutrient concentration - different combinations of parameters would turn the system into producing one composite or another, and it would perform this self-regulation by communicating within its network and restoring patterns stored in its systemic memory.