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Past iGEM Projects: Case Studies. 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

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2006 projects
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
2006 projects1
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
2006 projects2
2006 Projects:

Real World

  • University of Edinburgh: arsenic detector, (best real world, 3rd best device)
  • Slovenia: Sepsisprevention (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
edinburgh arsenic biosensor
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)
basic parts
Arsenate/arsenite

ArsR sensitive promoter

arsR gene

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!!

slide7
Arsenic sensor system diagram

8.5

Activator molecule A1

pH:

7.0

Activator gene

Lac regulator

6.0

4.5

A1 binding site

Lactose

Urease gene

Promoter

|A| |R|

(NH2)2CO + H2O = CO2 + 2NH3

R1 binding site

Repressor molecule R1

Ammonia

Arsenic (5ppb)

LacZ gene

Repressor gene R1

Ars regulator 1

Urease enzyme

LacZ enzyme

Lactic Acid

Arsenic (20ppb)

Ars regulator 2

results
Results:
  • Can detect WHO guideline levels of arsenate
  • Average overnight difference of 0.81 pH units
  • Response time of 5 hrs
take home message part 1
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
slovenia sepsis prevention
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
altering signaling pathway
Altering Signaling Pathway

PAMPs  TLR MyD88  IRAK4  NFκB  cytokines

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

CellDesigner:

http://www.systems-biology.org/cd/

measurements results
Measurements / Results
  • Flow cytometry: antibody to phosphorylated ERK kinase to detect TLR activation
  • Luciferase and ELISA assays: level of NF-kB
  • Microscopy
take home message part 2
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
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