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(The Gram Positve BugBuster)
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(The Gram Positve BugBuster)
 
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=====Newcastle University iGEM 2008=====
=====Newcastle University iGEM 2008=====
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===The Gram Positve BugBuster===
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===The Gram Positive BugBuster===
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We are developing a diagnostic tool that enables the detection of a range of gram-positive bacterial pathogens endemic in patients around the world. Currently detection of these pathogens ranges from a few hours to several days. The system will use genetically engineered Bacillus subtilis, to detect a range of gram positive bacterial pathogens. Our principal targets at this stage are Staphylococcus aureus, and Streptococcus pneumonia. We are also considering the detection of a range of Bacillus species, Clostridium difficile, and Staphylococcus epidermis. This new system will enable visual detection of these pathogens within minutes. The bacteria will be detected by the Quorum sensing peptides that they secrete extracellularly. This will activate the expression of fluorescence proteins in our engineered Bacillus subtilis chassis that can be viewed under U.V light.
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We are developing a diagnostic tool that enables the detection of a range of Gram-positive bacterial pathogens endemic in patients around the world. Currently detection of these pathogens ranges from a few hours to several days. The system will use genetically engineered ''Bacillus subtilis'', to detect a range of Gram-positive bacterial pathogens. Our principal targets at this stage are ''Staphylococcus aureus'', and ''Streptococcus pneumoniae''. We are also considering the detection of a range of ''Bacillus'' species, and ''Clostridium difficile''. This new system will enable visual detection of these pathogens within minutes. The bacteria will be detected by the specific quorum-sensing peptides that they secrete extracellularly. Detection of the quorum-sensing peptides will activate the expression of fluorescent proteins, viewable under U.V light, in our engineered ''B. subtilis'' chassis. This application would have importance not only in hospital settings, but also in the third world.
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Our Bioinformatics approach will involve the production of a workbench that will incorporate a designed parts repository, constraints repository and an evolutionary algorithm. The EA will input from the parts repository and constraints repository to carry out a neural network simulation. Eventually the fittest model will be output that can be used to produce a DNA sequence. This will be synthesized and cloned into the Bacillus subtilis chassis.
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An important part of our approach is bioinformatics. We will produce a workbench that will incorporate a parts repository, constraints repository and an evolutionary algorithm (EA). The EA will take input from the parts repository and constraints repository to evolve a neural network simulation. The fittest model will be used to generate a DNA sequence which will implement the neural network ''in vivo''. This DNA sequence will be synthesized and cloned into the ''B. subtilis'' chassis. One of our outcomes will be a range of neural network node BioBrick devices which can be combined to form the ''in vivo'' neural network.  
[[IGEM:NCL/2008|Main]]
[[IGEM:NCL/2008|Main]]

Current revision

Newcastle University iGEM 2008

The Gram Positive BugBuster

We are developing a diagnostic tool that enables the detection of a range of Gram-positive bacterial pathogens endemic in patients around the world. Currently detection of these pathogens ranges from a few hours to several days. The system will use genetically engineered Bacillus subtilis, to detect a range of Gram-positive bacterial pathogens. Our principal targets at this stage are Staphylococcus aureus, and Streptococcus pneumoniae. We are also considering the detection of a range of Bacillus species, and Clostridium difficile. This new system will enable visual detection of these pathogens within minutes. The bacteria will be detected by the specific quorum-sensing peptides that they secrete extracellularly. Detection of the quorum-sensing peptides will activate the expression of fluorescent proteins, viewable under U.V light, in our engineered B. subtilis chassis. This application would have importance not only in hospital settings, but also in the third world.

An important part of our approach is bioinformatics. We will produce a workbench that will incorporate a parts repository, constraints repository and an evolutionary algorithm (EA). The EA will take input from the parts repository and constraints repository to evolve a neural network simulation. The fittest model will be used to generate a DNA sequence which will implement the neural network in vivo. This DNA sequence will be synthesized and cloned into the B. subtilis chassis. One of our outcomes will be a range of neural network node BioBrick devices which can be combined to form the in vivo neural network.

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