Transcript Past iGEM Projects: Case Studies

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
• 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 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 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
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
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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
arsR gene codes for repressor that bind to arsenic promoter in absence of
arsenate/arsenite
Arsenate/arsenite
ArsR sensitive
promoter
arsR
gene
Link to LacZ, metabolism of lactose creates acidified medium 
decreased pH
Pars
arsR
lacZ
Sensitivity!!
Arsenic sensor system diagram
8.5
Lactose
Lac regulator
Activator gene
pH:
7.0
6.0
Activator molecule A1
4.5
A1 binding site
Urease gene
Promoter
|A|
Urease enzyme
|R|
(NH2)2CO + H2O = CO2 + 2NH3
Repressor molecule R1
R1 binding site
Ammonia
Arsenic (5ppb)
Ars regulator 1
Repressor gene R1
LacZ enzyme
Arsenic (20ppb)
Ars regulator 2
LacZ gene
Lactic Acid
System Design
Results:
Increased As sensitivity range: time against pH
7.5
0 ppb
5 ppb
7
15 ppb
30 ppb
6.5
45 ppb
pH
60 ppb
6
5.5
• Can detect WHO
guideline levels of
arsenate
• Average overnight
difference of 0.81
pH units
• Response time of
5 hrs
5
4.5
0
200
400
600
800
1000
1200
Time (in min)
1400
1600
1800
2000
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
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
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
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Flow cytometry: antibody to phosphorylated ERK kinase to detect TLR
activation
Luciferase and ELISA assays: level of NF-kB
Microscopy
26 new BioBricks for Mammalian Cells
Registration number
Part's Name
BBa_J52008
rluc
BBa_J52024
NFκB+dnMyD88-linker-rLuc-linkPEST191
BBa_J52010
NFκB
BBa_J52026
dnMyD88-linker-GFP
BBa_J52011
dnMyD88-linker-rLuc
BBa_J52027
NFκB+dnMyD88-linker-GFP
BBa_J52012
rluc-linker-PEST191
BBa_J52028
GFP-PEST191
BBa_J52013
dnMyD88-linker-rluc-linkpest191
BBa_J52029
NFκB+GFP-PEST191
BBa_J52014
NFκB+dnMyD88-linker-rLuc
BBa_J52034
CMV
BBa_J52016
eukaryotic terminator
BBa_J52035
dnMyD88
BBa_J52017
eukaryotic terminator vector
BBa_J52036
NFκB+dnMyD88
BBa_J52018
NFκB+rLuc
BBa_J52038
CMV-rLuc
BBa_J52019
dnTRAF6
BBa_J52039
CMV+rLuc-linker-PEST191
BBa_J52021
dnTRAF6-linker-GFP
BBa_J52040
CMV+GFP-PEST191
BBa_J52022
NFκB+dnTRAF6-linker-GFP
BBa_J52642
GFP
BBa_J52023
NFκB+rLuc-linker-PEST191
BBa_J52648
CMV+GFP
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