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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

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Past iGEM Projects: Case Studies

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  1. Past iGEM Projects: Case Studies

  2. 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

  3. 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

  4. 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

  5. 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)

  6. 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!!

  7. 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

  8. System Design

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

  10. 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

  11. 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

  12. 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/

  13. Measurements / Results • Flow cytometry: antibody to phosphorylated ERK kinase to detect TLR activation • Luciferase and ELISA assays: level of NF-kB • Microscopy

  14. 26 new BioBricks for Mammalian Cells

  15. 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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