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

Introduction

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Associative Memory
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Objectives

The objective of this project is to build an associative memory network using E.coli populations. The system will be built in order to memorize a given pattern and should be able to converge to this pattern when exposed to a similar one. This is a demonstration of a 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. For example, Quian et al. [1] built a small neural network using DNA strand displacement cascades. They implemented a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern.

The final goal of this project is to build an associative memory network in a populational system of E.coli. In our design, a population of bacteria represents a “neuron” of the network. Each neuron communicates with the whole network by quorum sensing molecules (QSM). Once all "neurons" are connected, it defines a called Hopfield associative memory architecture. The interactions can be repressive or excitatory, which means that one neuron can inhibit another one, repressing its production of QSM, or excite it, stimulating its production of QSM.

Application

In addition to be one of the first Hopfield Network models made in vivo, this project shows different lineages of bacteria communicating with each other, establishing balance through their 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 - it would perform that by communicating within its network and restoring the pattern stored in its systemic memory. In the future, self-controlled biosystems will be possible, cheap and ecologically friendly alternatives for the industry.