Team:USP-UNESP-Brazil/Associative Memory/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. This system will recognize a given visual pattern and answer it in a previously determined way, showing a demonstration of systemic memory storage from a synthetic biological system.

Justificative

Memory storage capacity and cellular communication have a central role in the development and the behavior of biological systems.Actually, storing and controlling information have a central role that impacts all areas, being crucial to the economic and social development.

Synthetic biology presents itself as a powerful tool on the construction of mechanisms capable of execute routines of processing and storing information in vivo on similar ways that are made in silico. For example, Quian et al built a biological system capable of recognize one person in a group of four people by identifying patterns. In this associative memory network, four neurons made of DNA molecules parts associated a sequence of four answers “yes” or “no”.Each pattern represented one person and could be remembered each time that the right sequence was inserted.

The final objective of the project is to build an associative memory network in a populational system of E.coli that could recognize visual patterns as input, therefore demonstrating the perception, storage, processing and answering in a logical way by a synthetic biological system. In order to do it, 9 different populations will be made, representing each one a “neuron” of the network. A population producing GFP (green fluorescent protein) will represent a neuron sending an action potential and each neuron will communicate with the whole network, what, in reality, defines the Hopfield associative memory architecture. In the interaction, a neuron can inhibit another one, leading it to a stop in the production of GFP, or exciting, stimulating the production of GFP.

The inhibition or excitement of GFP production will be based on a transcriptional regulation mechanism. The communication between bacterial populations will occur by means of quorum sensing substances and the information (inhibiting or exciting) will be defined by which transcriptional regulator the substance will promote. In summary, at the moment when the connections between the neurons are defined, we hope the system will output a predetermined pattern of answer.

Appliance

In addition to be one of the first Hopkins Network model made in vivo, this project shows different lineages of bacteria communicating with each other using its “systemic memories” in order to establish a balance. This could be used in the production of bio products, such as biofuels, in a way that, for example, inside a reactor, a bio system responsible for the production of a certain compound could auto regulates itself when some parameter changes, such as temperature or nutrients concentration. It would use its systemic memory, communicating within its network and restoring the pattern. In future, self-controlled bio systems will be able to be possible cheap and ecologically friendly alternatives in the industry.