Past igem projects case studies
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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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Past iGEM Projects: Case Studies

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Past igem projects case studies

Past iGEM Projects: Case Studies


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!!


Past igem projects case studies

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


Past igem projects case studies

System Design


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


26 new biobricks for mammalian cells

26 new BioBricks for Mammalian Cells


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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