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IGEM 2007 ETH Zurich 04.06.2007. ETH Zurich iGEM Team 2 ETH Zurich team.

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Presentation on theme: "IGEM 2007 ETH Zurich 04.06.2007. ETH Zurich iGEM Team 2 ETH Zurich team."— Presentation transcript:

1 iGEM 2007 ETH Zurich

2 ETH Zurich iGEM Team 2 ETH Zurich team

3 Learning Memory Recognition

4 Learning Memory Recognition

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

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

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

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

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

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

11 Memory Memory Input 1Memory Input 2 Latch State variable 1State variable

12 Memory Memory Input 1Memory Input 2 Latch State variable 1State variable xx0keep state

13 Gated SR with latch

14 Mapping with AND gates

15 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

16 Biological Implementation of our system

17 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

18 System in the initial state (without any chemicals present)

19 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

20 Learning aTc

21 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

22 Memorizing

23 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

24 Testing for aTc

25 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

26 Testing for IPTG

27 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

28 Equations 28

29 Parameters 29

30 Simulation of Equations 30

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

32 Sensitivity Analysis

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

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

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

36 Acknowledgments 36

37 Thank you! Thank you for your attention! Questions?

38 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

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

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

41 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

42 Introduction, Motivation 3 Phases Learning Memory Recognition 42

43 Lab work BD FACSAria™ Cell-Sorting System


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