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Team:USP-UNESP-Brazil/Associative Memory/Introduction
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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 | + | 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
IntroductionProject OverviewPlasmid Plug&PlayAssociative MemoryNetwork
ExtrasObjectives
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, demonstrating how a systemic memory storage can be designed using 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, 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 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 the system should return a predetermined response pattern.
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.
