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

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(Biological Mechanism)
 
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=Background=
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===Hopfield Associative Memory Networks===
===Hopfield Associative Memory Networks===
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===Biological Mechanism===
===Biological Mechanism===
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In a biological neural network, cells occupy a specific location and the information is passed through direct physical contact - the neuron axonal projections. In our case, a population of bacteria represents a single neuron and the information is transmitted by a quorum sensing molecule (QSM). With different QSM, it is possible to transmit different kinds of messages. A comparison between a biological neural network and our design is presented in Fig 1.  
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In a biological neural network, cells occupy a specific location and the information is passed through direct physical contact - the neuron axonal projections. In our case, a population of bacteria represents a single neuron and the information is transmitted by a quorum sensing molecule (QSM). Because of that, each "neuron" has its own QSM and the number of neurons is limitated by the number of different QSM. A comparison between a biological neural network and our design is presented in Fig 1.  
{{:Team:USP-UNESP-Brazil/Templates/RImage | image=Figura0020.jpg | caption=Fig 1. Comparison between a biological neural network and "bacterial neural network" | size=600px}}
{{:Team:USP-UNESP-Brazil/Templates/RImage | image=Figura0020.jpg | caption=Fig 1. Comparison between a biological neural network and "bacterial neural network" | size=600px}}
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{{:Team:USP-UNESP-Brazil/Templates/RImage | image=Physicalsystemforbacterialnetwork.png | caption=Fig 2 | size=600px}}
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{{:Team:USP-UNESP-Brazil/Templates/RImage | image=Physicalsystemforbacterialnetwork.png | caption=Fig 2. Device that will be used to measure the output. | size=600px}}
====Genetic Construction====
====Genetic Construction====
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Each population of bacteria ("neuron") is defined by the way it interacts within the network - by its own QSM. Hence, the number of "neurons" is limited to the number of QSM. As a proof of concept we designed two populations of bacteria that communicate in a repressive manner. In order to make the network visually interesting, we used our 3x3 wells device and built the population network to recognize two patterns - Figure 3. Since they are complementary, only two different population of bacteria are needed to represent the patterns "X" and "O". In this case, each population placed at the letter "X" inhibits all the ones placed at the letter “O” and activates the positions of its own pattern (and vice-versa).
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Each population of bacteria ("neuron") is defined by the way it interacts within the network and by its own QSM. Hence, the number of "neurons" is limited to the number of QSM. As a proof of concept we designed two populations of bacteria that communicate in a repressive manner. In order to make the network visually interesting, we used our 3x3 wells device and designed the population network to recognize two patterns - Figure 3. Since they are complementary, only two different population of bacteria are needed to represent the patterns "X" and "O". In this case, each population placed at the letter "X" inhibits all the ones placed at the letter “O” and activates the positions of its own pattern (and vice-versa).
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{{:Team:USP-UNESP-Brazil/Templates/RImage | image=0019.JPG | caption=Fig. 3 | size=600px}}
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{{:Team:USP-UNESP-Brazil/Templates/RImage | image=0019.JPG | caption=Fig. 3. Representation of the input and output in the 3x3 wells device. | size=600px}}
In the Registry of Parts [http://partsregistry.org/Main_Page] there are four well characterized quorum sensing systems. However, there is a strong activation crosstalk between two of them (Las and Rhl). Therefore, we decided to use the system Cin and Rhl.
In the Registry of Parts [http://partsregistry.org/Main_Page] there are four well characterized quorum sensing systems. However, there is a strong activation crosstalk between two of them (Las and Rhl). Therefore, we decided to use the system Cin and Rhl.
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As an example, if an input activates more positions at the “X” pattern than those at the “O”, a greater number of "X-activators" will be produced, and it is more likely that other "X" positions become activated, due to this competition for promoters at each position. Meanwhile, in the “O” pattern positions, the opposite occurs: a lesser number of initially activated positions implies less "O-activators", and the outcome is that "X wins over O" - the network reproduces the "X" pattern.
As an example, if an input activates more positions at the “X” pattern than those at the “O”, a greater number of "X-activators" will be produced, and it is more likely that other "X" positions become activated, due to this competition for promoters at each position. Meanwhile, in the “O” pattern positions, the opposite occurs: a lesser number of initially activated positions implies less "O-activators", and the outcome is that "X wins over O" - the network reproduces the "X" pattern.
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This multi-regulated promoter, with an activator and an inhibitor, is called Prm. Its inhibitor is the transcriptional factor cl434 and its activator is the cl factor. The genetic design of the positions of the patterns “X” and “O” can be seen in Figure 6.
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This multi-regulated promoter, with an activator and an inhibitor, is called Prm. Its inhibitor is the transcriptional factor cl434 and its activator is the cl factor. The genetic design of the positions of the patterns “X” and “O” can be seen in Figure 4.
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This construction using the signal transduction containing cl434 and cl allows creating different systems of associative memory, limited only by the number of quorum sensing systems available. Figure 6 shows how this generic system would work and elucidates how this system could be applied to different functions involving genetic control.
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This construction that uses the signal transduction containing cl434 and cl allows to create different systems of associative memory, limited only by the number of quorum sensing systems available. Figure 4 shows how this generic system would work and elucidates how this system could be applied to different functions involving genetic control.
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[[File:0022.png|center|600px|caption=Fig. 4|]]
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{{:Team:USP-UNESP-Brazil/Templates/RImage | image=0022.png | caption=Fig. 4. Genetic construction. | size=600px}}
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Latest revision as of 03:47, 27 September 2012