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iGEM 2007 ETH Zurich 04.06.2007

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ETH Zurich iGEM Team 2 ETH Zurich team

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Learning Memory Recognition

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Learning Memory Recognition

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System design System input 1 System input 2 System output 1 System output 2 System output 3 System output 4 SensorsDecoderMemory

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Memory Input 1Memory Input 2State variable 1State variable 2

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Memory Memory Input 1Memory Input 2State variable 1State variable 2 0000

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Memory Memory Input 1Memory Input 2State variable 1State variable 2 0000 1010

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Memory Memory Input 1Memory Input 2State variable 1State variable 2 0000 1010 0101

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Memory Memory Input 1Memory Input 2State variable 1State variable 2 0000 1010 0101 How can the switch keep its state with a new input?

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Memory Memory Input 1Memory Input 2 Latch State variable 1State variable 2 00100 10110 01101

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Memory Memory Input 1Memory Input 2 Latch State variable 1State variable 2 00100 10110 01101 xx0keep state

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Gated SR with latch

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Mapping with AND gates

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System design System input 1 System input 2 System output 1 System output 2 System output 3 System output 4 Sensors Decoder Memory Latch Sensor 1 Sensor 2 Sensor 3 aTc IPTG AHL TetR LuxR LacI CFP RFP YFP GFP cI cII

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Biological Implementation of our system

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cI P const lacI cI P const tetR cI P const luxR cI P const cII O CI O LuxR cI O LuxR O CII cI P const O CII O TetR cI P const O LacI O CI cI P const cI O CII O lacI cI P const cII O TetR O CI RFP GFP CFP YFP P const System overview

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System in the initial state (without any chemicals present)

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cI P const lacI cI P const tetR cI P const luxR cI P const cII O CI O LuxR cI O LuxR O CII cI P const O CII O TetR cI P const O LacI O CI cI P const cI O CII O lacI cI P const cII O TetR O CI RFP GFP CFP YFP TetR LacI LuxR TetR LacI P const LacITetR

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Learning aTc

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cI P const lacI cI P const tetR cI P const luxR cI P const cII O CI O LuxR cI O LuxR O CII cI P const O CII O TetR cI P const O LacI O CI cI P const cI O CII O lacI cI P const cII O TetR O CI RFP GFP CFP YFP TetR LacI LuxR TetR LacI P const LacI aTc CII TetR

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Memorizing

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cI P const lacI cI P const tetR cI P const luxR cI P const cII O CI O LuxR cI O LuxR O CII cI P const O CII O TetR cI P const O LacI O CI cI P const cI O CII O lacI cI P const cII O TetR O CI RFP GFP CFP YFP TetR LacI LuxR TetR LacI P const LacI aTc CII TetR AHL + CII

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Testing for aTc

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cI P const lacI cI P const tetR cI P const luxR cI P const cII O CI O LuxR cI O LuxR O CII cI P const O CII O TetR cI P const O LacI O CI cI P const cI O CII O lacI cI P const cII O TetR O CI RFP GFP CFP YFP TetR LacI LuxR TetR LacI P const LacI aTc CII TetR AHL + CII TetR CII

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Testing for IPTG

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cI P const lacI cI P const tetR cI P const luxR cI P const cII O CI O LuxR cI O LuxR O CII cI P const O CII O TetR cI P const O LacI O CI cI P const cI O CII O lacI cI P const cII O TetR O CI RFP GFP CFP YFP TetR LacI LuxR TetR LacI P const CII TetR AHL + CII TetR IPTG LacI

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Equations 28

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Parameters 29

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Simulation of Equations 30

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Sensitivity Questions – Parameter accuracy? – “Dangerous” parameters? – Target parameters for biological changes?

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Sensitivity Analysis

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Lab work P const lacI P const O LacI O CI GFP + LacI IPTG LacI

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Summary Learning, Memory, Recognition Successful System Simulations Realistic Parameters – Robust Design Toggle switch design – dual promoter 11 Parts to registry 34

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Applications Bio-Memory Bio-Chip Multiple Purpose Cell Lines – Patient Specific Medicine – Intelligent Biosensors 35

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Acknowledgments 36

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Thank you! Thank you for your attention! Questions?

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Sensitivity analysis results Robustness System sensitive to: – Protein basal production levels (???) – Parameters elated to the cI, cII function cI, cII repressors dissociation constant cI, cII repressors Hill cooperativity cI, cII degradation rates Candidates for biological changes: – Basal production levels – cI, cII degradation rates

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Sensors System input 1 System input 2 Sensors Memory input 1 Memory input 2 Sensor 1 Sensor 2

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Memory Memory input 1 Memory input 2 Memory output 1 Memory output 2 ? State variable 1 State variable 2

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Decoder State variable 1 State variable 2 System output 1 System output 2 System output 3 System output 4 Current input 1 in2 ANDsv2 in2ANDsv1 in1 ANDsv2 in1 ANDsv1

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Introduction, Motivation 3 Phases Learning Memory Recognition 42

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Lab work BD FACSAria™ Cell-Sorting System

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