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

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===Objectives===
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=Introduction=
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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.
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==Objectives==
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===Justificative===
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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.
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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.
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==Background==
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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.
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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. [http://www.nature.com/nature/journal/v475/n7356/full/nature10262.html?WT.ec_id=NATURE-20110721] 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.  
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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.
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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) is defined by which transcriptional regulator the substance promotes. "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.
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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.
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==Application==
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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.
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Latest revision as of 02:30, 27 September 2012

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