Team:Newcastle University

From 2008.igem.org

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'''Welcome!'''
'''Welcome!'''
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This year is Newcastle's first year participating in the iGEM competition! We are a small [[Team:Newcastle_University/Team|team]] of six students, three supervisors, and four advisors, but we have high ambitions for our first year. Recognizing the problem of antibiotic resistant bacteria and poor methods of diagnosis in underdeveloped countries, we aim to use synthetic biology and bioinformatics to engineer a simple, safe, fast, and reliable biological diagnostic system to identify bacteria.
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This year is Newcastle's first year participating in the iGEM competition! We are a small [[Team:Newcastle_University/Team|team]] of six students, three supervisors, and seven advisors, but we had high ambitions for our first year. Recognizing the problem of antibiotic-resistant bacteria and poor methods of diagnosis in developing countries, we aim to use synthetic biology and bioinformatics to engineer a simple, safe, fast, and reliable biological diagnostic system to identify bacteria.
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We aimed to develop a diagnostic biosensor for detecting pathogens. We wanted this to be cheaply and readily available for deployment in areas with limited medical resources such as refrigeration and sophisticated laboratories. We chose to use ''Bacillus subtilis'' as a method of delivery due to its ability to sporulate. The sensor bacteria could then be dried down as spores, which are extremely resilient to environmental conditions, and then could be rehydrated when required.
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==Biological Theme: Bug Busters==
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Gram-positive bacteria communicate using quorum communication peptides. Research has shown that they are extremely strain-specific. We chose to engineer a ''Bacillus'' 168 to detect four Gram-positive pathogens through their quorum communication peptides. The output of the system is expression of three fluorescent proteins: mCherry, GFP, and YFP.
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[[Image:Bug-small.png|left]]
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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. This new system will enable visual detection of these pathogens within minutes. This application would have importance not only in hospital settings, but also in the third world.
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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 ''Bacillus 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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Mapping multiple inputs to three output states is a multiplexing problem. The design of the genetic circuitry to do this is non-trivial and is not feasible manually. We therefore chose to use a biological implementation of an artificial neural network (ANN). Our team members wrote designed and implemented a complete suite of tools that allowed the design and simulation of regulatory networks. An essential part of the approach was the use of computational evolution to design circuits with predictable behaviour even when the details of the required topology are unknown in advance. We used the modelling language CellML because of its ability to easily model virtual parts that can be easily assembled into a circuit which can be simulated. We aimed to translate this model into a sequence that could be implemented as BioBricks.
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[[Team:Newcastle_University/Parallel Evolution|Learn more about our project]]
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[[Team:Newcastle_University/Parallel Evolution|Learn more about our project]] }}
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== ''Bacillus subtilis'' ==  
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''B. subtilis'' is our favorite bacteria to work with because it is gram positive, relatively safe to work with, is naturally competent, naturally sporulates, and is well-characterised for experimentation.
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|title=Achievements
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[[Team:Newcastle_University/Bacillus_subtilis|Learn more about ''B. subtilis'']].
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<li>We designed and submitted a [http://partsregistry.org/Part:BBa_K104001 working standard BioBrick] for sensing the quorum communication peptide subtilin, that [[Team:Newcastle University/Notebook|works as expected]].
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<li>Sent information and developed a [[Team:Newcastle_University/Bacillus_subtilis|''B. subtilis'' website]].to help Cambridge
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<li>Developed and documented a new technical standard using CellML for the modelling and simulation of BioBrick parts
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== Sponsors ==
== Sponsors ==
{{:Team:Newcastle University/Sponsors}}
{{:Team:Newcastle University/Sponsors}}
{{:Team:Newcastle University/HomeSideBar}}
{{:Team:Newcastle University/HomeSideBar}}

Revision as of 11:44, 28 October 2008

Bugbuster-logo-red.png
Ncl uni logo.jpg


Newcastle University

GOLD MEDAL WINNER 2008

Home Team Original Aims Software Modelling Proof of Concept Brick Wet Lab Conclusions


Welcome! This year is Newcastle's first year participating in the iGEM competition! We are a small team of six students, three supervisors, and seven advisors, but we had high ambitions for our first year. Recognizing the problem of antibiotic-resistant bacteria and poor methods of diagnosis in developing countries, we aim to use synthetic biology and bioinformatics to engineer a simple, safe, fast, and reliable biological diagnostic system to identify bacteria.


Overview

We aimed to develop a diagnostic biosensor for detecting pathogens. We wanted this to be cheaply and readily available for deployment in areas with limited medical resources such as refrigeration and sophisticated laboratories. We chose to use Bacillus subtilis as a method of delivery due to its ability to sporulate. The sensor bacteria could then be dried down as spores, which are extremely resilient to environmental conditions, and then could be rehydrated when required.

Gram-positive bacteria communicate using quorum communication peptides. Research has shown that they are extremely strain-specific. We chose to engineer a Bacillus 168 to detect four Gram-positive pathogens through their quorum communication peptides. The output of the system is expression of three fluorescent proteins: mCherry, GFP, and YFP.

Mapping multiple inputs to three output states is a multiplexing problem. The design of the genetic circuitry to do this is non-trivial and is not feasible manually. We therefore chose to use a biological implementation of an artificial neural network (ANN). Our team members wrote designed and implemented a complete suite of tools that allowed the design and simulation of regulatory networks. An essential part of the approach was the use of computational evolution to design circuits with predictable behaviour even when the details of the required topology are unknown in advance. We used the modelling language CellML because of its ability to easily model virtual parts that can be easily assembled into a circuit which can be simulated. We aimed to translate this model into a sequence that could be implemented as BioBricks.

Learn more about our project


Achievements

  • We designed and submitted a working standard BioBrick for sensing the quorum communication peptide subtilin, that works as expected.
  • Sent information and developed a B. subtilis website.to help Cambridge
  • Developed and documented a new technical standard using CellML for the modelling and simulation of BioBrick parts

Sponsors

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