Team:Princeton/Project

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(Princeton iGEM Utility Belt)
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In the second application we are attempting to teach networks of neurons to recognize inputs in the form of neurotransmitters. By “teach,” we mean using genetic feedback mechanisms to strengthen the synapses that lead to the “correct” answer and weaken all others, thus making our network responsive only to the inputs we want it to recognize.
In the second application we are attempting to teach networks of neurons to recognize inputs in the form of neurotransmitters. By “teach,” we mean using genetic feedback mechanisms to strengthen the synapses that lead to the “correct” answer and weaken all others, thus making our network responsive only to the inputs we want it to recognize.
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== Project Details==
 
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===Plasmid Designs===
 
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We worked on designing and constructing several plasmids this summer. We will shortly put up a list of how far we got with each of them over the course of the summer, and what work remains to be done.
 
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=== Experiments ===
 
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===Princeton iGEM Utility Belt===
 
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As part of the project, we have started work on two novel combinations of biology and electrical engineering.
 
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'''Surface Patterning'''
 
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[https://2008.igem.org/Image:AssafRotemReasearch_Description.pdf It has been shown] that plating neurons in a specific pattern gives the emerging networks special properties, which are determined by those patterns. Also, the growth of these networks is strongly influenced by the patterns they are allowed to grow in. Therefore, by patterning the surfaces that neurons grow on, we can direct specific network connections. This is at the heart of the architecture of the toggle switch.
 
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'''Optical Tweezers'''
 
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== Results ==
 

Revision as of 16:25, 15 August 2008

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PRINCETON IGEM 2008

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Overall project

The goal of the Princeton iGEM team is to utilize the immense capabilities of neurons and in particular of neuronal networks – in terms of efficiency, speed of transmission of information and output, physical size, robustness, reliability, and programmable versatility – by taking two very different approaches to neuronal networks using gene-regulatory circuits.


The first application is a toggle switch, which holds an output stable in one of two states. This is modeled along the lines of a simple RS latch from digital logic. There is simultaneous, continuous activation of two clusters of neurons by pacemaker cells, where the two clusters cross-repress/mutually inhibit each other. The simultaneous activation by the pacemaker cells and the repression activated in one cluster of neurons at a time, allows the other cluster to be held in a stable state. This allows us to build a memory element similar to that proposed by Gardner et al. that is orders of magnitude faster.


In the second application we are attempting to teach networks of neurons to recognize inputs in the form of neurotransmitters. By “teach,” we mean using genetic feedback mechanisms to strengthen the synapses that lead to the “correct” answer and weaken all others, thus making our network responsive only to the inputs we want it to recognize.