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

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===Background===
===Background===
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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'', in similar ways to what is done ''in silico''. For example, Quian et al. [http://www.nature.com/nature/journal/v475/n7356/full/nature10262.html?WT.ec_id=NATURE-20110721] built a biological system capable of recognizing 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'', in similar way to what is done ''in silico''. For example, Quian et al. [http://www.nature.com/nature/journal/v475/n7356/full/nature10262.html?WT.ec_id=NATURE-20110721] built a biological system capable of recognizing 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 goal of this project is to build an associative memory network in a populational system of E.coli, that is able to  recognize visual patterns as input. This system, therefore, demonstrates perception, storage and processing of information done by a synthetic biological system.  In order to make it, nine different populations will be prepared, representing each one a “neuron” of the network.  Each neuron will communicate with the whole network through quorun sensing signalling, which defines the Hopfield associative memory architecture. In the interaction, a neuron can inhibit another one, leading its production of GFP to a halt, or excite it, stimulating its production of GFP.   
The final goal of this project is to build an associative memory network in a populational system of E.coli, that is able to  recognize visual patterns as input. This system, therefore, demonstrates perception, storage and processing of information done by a synthetic biological system.  In order to make it, nine different populations will be prepared, representing each one a “neuron” of the network.  Each neuron will communicate with the whole network through quorun sensing signalling, which defines the Hopfield associative memory architecture. In the interaction, a neuron can inhibit another one, leading its production of GFP to a halt, or excite it, stimulating its production of GFP.   

Revision as of 22:58, 25 September 2012