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, 3 rd best device) Slovenia: Sepsis prevention (grand prize winner, 2 nd best system) Latin America: UV-iron interaction biosensor Mississippi State University: H 2 reporter Prairie View: Trimetallic sensors Princeton: Mouse embryonic stem cell differentiation using artificial signaling pathways (2 nd 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 –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)

arsR gene codes for repressor that bind to arsenic promoter in absence of arsenate/arsenite Basic Parts Link to LacZ, metabolism of lactose creates acidified medium  decreased pH ArsR sensitive promoter arsR gene Arsenate/arsenite P ars arsRlacZ Sensitivity!!

Lac regulatorActivator gene Activator molecule A1 Lactose |A| |R|Promoter Urease gene A1 binding site Urease enzyme (NH 2 ) 2 CO + H 2 O = CO 2 + 2NH 3 Ars regulator 1Repressor gene R1 Arsenic (5ppb) Ars regulator 2LacZ gene Repressor molecule R1 Arsenic (20ppb) LacZ enzyme R1 binding site Arsenic sensor system diagram pH: Ammonia Lactic Acid

System Design

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): 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 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: PAMPs  TLR  MyD88  IRAK4  NFκB  cytokines

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 Registration numberPart's Name BBa_J52008rluc BBa_J52010NFκB BBa_J52011dnMyD88-linker-rLuc BBa_J52012rluc-linker-PEST191 BBa_J52013 dnMyD88-linker-rluc-link- pest191 BBa_J52014NFκB+dnMyD88-linker-rLuc BBa_J52016eukaryotic terminator BBa_J52017eukaryotic terminator vector BBa_J52018NFκB+rLuc BBa_J52019dnTRAF6 BBa_J52021dnTRAF6-linker-GFP BBa_J52022NFκB+dnTRAF6-linker-GFP BBa_J52023NFκB+rLuc-linker-PEST191 BBa_J52024 NFκB+dnMyD88-linker-rLuc-link- PEST191 BBa_J52026dnMyD88-linker-GFP BBa_J52027NFκB+dnMyD88-linker-GFP BBa_J52028GFP-PEST191 BBa_J52029NFκB+GFP-PEST191 BBa_J52034CMV BBa_J52035dnMyD88 BBa_J52036NFκB+dnMyD88 BBa_J52038CMV-rLuc BBa_J52039CMV+rLuc-linker-PEST191 BBa_J52040CMV+GFP-PEST191 BBa_J52642GFP BBa_J52648CMV+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