Past iGEM Projects: Case Studies

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Past iGEM Projects Case Studies
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2006 Projects

• Neat Gadgets
• University of Arizona Bacterial water color
• BU Bacterial nightlight
• Brown Bacterial freeze tag, tri-stable toggle
  switch
• University of Calgary Dance with swarms
• Chiba University, Japan Swimmy bacteria,
  aromatic bacteria
• Davidson Solving the pancake problem
• Duke Underwater power plant, cancer stickybot,
  human encryption, protein cleavage switch,
  xverter predator/prey
• Missouri Western State University Solving the
  pancake problem
• MIT Smelly bacteria (best system)
• Penn State Bacteria relay race (passing QS
  molecules off as batons)
• Purdue Live color printing
• Tokyo Alliance Bacteria that can play
  tic-tac-toe
• UCSF Remote control steering of bacteria through
  chemotaxis

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2006 Projects

• Research Tools
• Bangalore synching cell cycles, memory effects
  of UV exposure
• Berkeley riboregulator pairs, bacterial
  conjugation
• University of Cambridge Self-organized pattern
  formation
• Freiburg University DNA-origami
• ETH Bacterial adder
• Harvard DNA nanostructures, surface display,
  circadian oscillators
• Imperial College oscillator (great
  documentation)
• University of Michigan algal bloom, Op Sinks,
• McGill Split YFP / Repressilator
• Rice quorumtaxis
• University of Oklahoma Distributed sensor
  networks
• IPN_UNAM, Mexico cellular automata (simulations)
• University of Texas Edge detector

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2006 Projects

• Real World
• University of Edinburgh arsenic detector, (best
  real world, 3rd best device)
• Slovenia Sepsis prevention (grand prize winner,
  2nd best system)
• Latin America UV-iron interaction biosensor
• Mississippi State University H2 reporter
• Prairie View Trimetallic sensors
• Princeton Mouse embryonic stem cell
  differentiation using artificial signaling
  pathways (2nd runner up)
• University of Toronto Cell-see-us thermometer

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Edinburgh Arsenic Biosensor

• Goal Develop a bacterial biosensor that responds
  to a range of arsenic concentrations and produces
  a change in pH that can be calibrated in relation
  with the arsenic concentration.
• Lots of previous research into arsenic biosensors
• Gene promoters that respond to presence of
  arsenic
• Different outputs available
• pH is easy, practical, and cheap to measure
• Signal conversion A?B?C where C is easy to
  detect
• System Arsenate/arsenite ? detector ? reporter
  (pH change)

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Basic Parts
arsR gene codes for repressor that bind to
arsenic promoter in absence of arsenate/arsenite
Link to LacZ, metabolism of lactose creates
acidified medium ? decreased pH
Pars
arsR
lacZ
Sensitivity!!
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System Design
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Results

• Can detect WHO guideline levels of arsenate
• Average overnight difference of 0.81 pH units
• Response time of 5 hrs

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Take Home Message (part 1)

• Sensors are relatively straight-forward in design
  (A?B?C)
• I/O signal sensitivity is key
• Tight regulation of detector components
• Most of the components were available
  (engineering vs. research)
• Real world applications

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Slovenia Sepsis Prevention

• Goal Mimic natural tolerance to bacterial
  infections by building a feedback loop in TLR
  signaling pathway, which would decrease the
  overwhelming response to the persistent or
  repeated stimulus with Pathogen Associated
  Molecular Patterns (PAMPs).

• Engineering mammalian cells
• Medical application

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Altering Signaling Pathway
PAMPs ? TLR ? MyD88 ? IRAK4 ? NF?B ? cytokines
CellDesigner http//www.systems-biology.org/cd/

• MyD88 central protein of TLR signaling pathway
  that transfers signal from TLR receptor to
  downstream proteins (IRAK4) resulting in the NF?B
  activation
• Method
• Use dominant negative MyD88 to tune down
  signaling pathway to NF-?B
• Addition of degradation tags to dnMyD88 with PEST
  sequence ? temporary inhibition to NF-?B

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Measurements / Results

• Flow cytometry antibody to phosphorylated ERK
  kinase to detect TLR activation
• Luciferase and ELISA assays level of NF-kB
• Microscopy

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26 new BioBricks for Mammalian Cells
Registration number Part's Name
BBa_J52008 rluc
BBa_J52010 NF?B
BBa_J52011 dnMyD88-linker-rLuc
BBa_J52012 rluc-linker-PEST191
BBa_J52013 dnMyD88-linker-rluc-link-pest191
BBa_J52014 NF?BdnMyD88-linker-rLuc
BBa_J52016 eukaryotic terminator
BBa_J52017 eukaryotic terminator vector
BBa_J52018 NF?BrLuc
BBa_J52019 dnTRAF6
BBa_J52021 dnTRAF6-linker-GFP
BBa_J52022 NF?BdnTRAF6-linker-GFP
BBa_J52023 NF?BrLuc-linker-PEST191
BBa_J52024 NF?BdnMyD88-linker-rLuc-link-PEST191
BBa_J52026 dnMyD88-linker-GFP
BBa_J52027 NF?BdnMyD88-linker-GFP
BBa_J52028 GFP-PEST191
BBa_J52029 NF?BGFP-PEST191
BBa_J52034 CMV
BBa_J52035 dnMyD88
BBa_J52036 NF?BdnMyD88
BBa_J52038 CMV-rLuc
BBa_J52039 CMVrLuc-linker-PEST191
BBa_J52040 CMVGFP-PEST191
BBa_J52642 GFP
BBa_J52648 CMVGFP
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Take Home Message (part 2)

• Lessons from their team
• Use reliable oligo vendors
• Double check biobrick parts for incorrectly
  registered parts
• Lot of work to find out optimal parameters for
  cell activation (inducer conc., etc.)
• Mammalian cells are more challenging to work with
• Requires more sophisticated readouts
• Make new biobricks!
• Reward is great