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E. coli Automatic Directed Evolution Machine a Universal Framework for Evolutionary Approaches in Synthetic Biolo

E. coli Automatic Directed Evolution Machine a Universal Framework for Evolutionary Approaches in Synthetic Biology. University of Science and Technology of China. The power of evolution. Our inspiration. Biology. Engineering. Synthetic Biology.

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E. coli Automatic Directed Evolution Machine a Universal Framework for Evolutionary Approaches in Synthetic Biolo

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  1. E. coli Automatic Directed Evolution Machinea Universal Framework for Evolutionary Approaches in Synthetic Biology University of Science and Technology of China

  2. The power of evolution

  3. Our inspiration Biology Engineering • Synthetic Biology • E. coli Automatic Directed Evolution Machine Directed Evolution Evolutionary Algorithm

  4. The Goal Scoring Function Evolution Desired Result Evolution Object E.ADEM

  5. E.ADEM Framework Kill Variation Function Selection Function Change Evolution Object PoPS Variation PoPS Selection Self-Adaptive Controller Reporter PoPS Act On PoPS Score Scoring Function Score

  6. Demand Analysis

  7. The Prototype

  8. Scoring Function PoPS tetR luxI tetR Selection Function PoPS AHL pLux-Tet luxR-AHL luxR pCon 0.15 pCon 0.70 luxR luxI Self-Adaptive Controller

  9. Scoring Function PoPS tetR luxI tetR Selection Function PoPS AHL pLux-Tet luxR-AHL luxR pCon 0.15 pCon 0.70 luxR luxI Self-Adaptive Controller

  10. Scoring Function PoPS tetR luxI tetR Selection Function PoPS AHL pLux-Tet luxR-AHL luxR pCon 0.15 pCon 0.70 luxR luxI Self-Adaptive Controller

  11. Scoring Function PoPS tetR luxI tetR Selection Function PoPS AHL pLux-Tet luxR-AHL luxR pCon 0.15 pCon 0.70 luxR luxI Self-Adaptive Controller

  12. Selection function tetR pLux-Tet ccdB luxR-AHL

  13. Selection function High AHL & Low tetR High AHL & High tetR tetR tetR tetR tetR luxR luxR-AHL luxR-AHL AHL AHL pLux-Tet pLux-Tet pLux-Tet ccdB ccdB ccdB pLux-Tet ccdB Low AHL & Low tetR Low AHL & High tetR luxR AHL AHL

  14. High score E.coli AHL tetR VR X VR X mRFP mRFP pCon X pCon X tetR tetR luxI luxI pLux-Tet pLux-Tet ccdB ccdB Low score E.coli AHL tetR

  15. High score E.coli AHL tetR VR X mRFP pCon X tetR luxI pLux-Tet ccdB Low score E.coli AHL tetR VR X mRFP pCon X tetR luxI

  16. Measurement and Modeling

  17. Measurement and Modeling • Basic measurement strategy GFP Reporter B0015 PoPS B0034 Use spectrfluorophotometer to measure fluorescence intensity

  18. Measurement • Standard measurement Standard unit: ustc_st1 Strains : TOP10 Plasmid: pSB1A3 Medium: M9 supplemented medium Reporter: GFP BBa_I13504 More details about the measurement are available on our team wiki: http://2009.igem.org/Team:USTC/Standard_%26_Protocol

  19. VR X pCon X luxI mRFP tetR tetR pLux - Tet ccdB AHL luxR - AHL luxR pCon 0.15 pCon 0.70 luxR luxI Scoring Reporter Self - Adaptive Controller Selection Function Function

  20. Delayed Waiting Working Done Sequenced 1 5 2 6 7 Measurement • pLux-Tet • pLux-Tet + GFP • pCon×4+ luxR+ pLux-Tet • ccdB×8 • pCon×8 • pCon×8 + GFP • pCon+ luxR • pCon×7 + luxI (AHL detection by 9) • pCon×4 + luxR + pLux-Tet + GFP (AHL) • pCon×4 + luxR+ pLux-Tet + ccdB×8 (AHL) • tetR×2 • tetR×2 + pCon×2 + luxR + pLux-Tet [+ GFP] • pCon×4+ tetR×2 + pCon×2 + luxR + pLux-Tet[+ GFP] (AHL/aTc) • [pCon×7 +] luxI+ pCon×2 + luxR + pLux-Tet[+ ccdB×8 | + GFP] (AHL) • VR×10 • (VR + pCon)×7 • tetR + pCon + luxI + pCon+ luxR + pLux-Tet [+ ccdB | + GFP] • [mRFP +] luxI + tetR + pCon + luxI + pCon + luxR + pLux-Tet [+ ccdB | + GFP] • (VR + pCon)×5 + tetR+ pCon + luxI + pCon + luxR + pLux-Tet [+ ccdB | + GFP] • (VR + pCon)×5 + mRFP+ luxI + tetR + pCon + luxI+ pCon + luxR + pLux-Tet [+ ccdB | + GFP] 9 3 4 10 8 11 14 15 12 17 16 19 13 18 20

  21. Constitutive Promoter tetR VR X mRFP pCon X luxI tetR FLU/OD(ustc_st1) AHL pLux-Tet ccdB luxR pCon 0.15 pCon 0.70 luxR luxI

  22. Hybrid Promoter tetR VR X mRFP pCon X luxI tetR pLux-Tet AHL ccdB luxR pCon 0.15 pCon 0.70 luxR luxI Hybrid promoter as a logic gate

  23. Other measurement Hybrid promoter response to LuxI Hybrid promoter response to aTc Efficiency of cI/pcI inverter More details about the measurement are available on our team wiki: http://2009.igem.org/Team:USTC/Standard_%26_Protocol

  24. ccdB parts • Previous work: lacZα-ccdB • Did not work very well. Still working… • Different versions of ccdB we have tried B0015 B0015 B0031 lacZα-ccdB-LVA B0031 ccdB B0015 B0015 B0034 ccdB B0034 lacZα-ccdB-LVA B0015 B0031 B0015 ccdB-LVA lacZα-ccdB B0034 B0015 B0034 ccdB-LVA B0015 B0031 lacZα-ccdB You L, Cox RS 3rd, Weiss R, Arnold FH. Programmed population control by cell-cell communication and regulated killing. Nature. 2004 Apr 22;428(6985):868-71.

  25. Modeling pLux-Tet We estimate the values of k3, rAHLin, C1, C2, C3 and C4, using the data from our measurements. The simulation results as follows. k3=0.8min-1; rAHL=0.001min-1; C1=1.5*10-10; C2=1*1010; C3=8*10-7; C4=5.8*1011

  26. Simulation results The dots in the graph represent our experiment data and the curve and surface represent our simulation results.

  27. Summary ★ Submit over 160 BioBrick parts to the registry ★ Characterize most of the parts ★ Comprehensive measurement data for whole system modeling ★ Introduce evolutionary algorithm into synthetic biology ★ A modular framework for automatic in vivo directed evolution

  28. Future Plan • Short-term plan: • Use GFP to check the output of the self-adaptive controller • Try alternative proposal of the selection function, such as antisense RNA

  29. PoPS GFP

  30. Future Plan • Short-term plan: • Use GFP to check the output of the self-adaptive controller • Try alternative proposal of the selection function, such as antisense RNA

  31. Future Plan • Long-term plan: • E.ADEM as collaborative project in OpenWetWare • Realize the scoring functions for transcription repressor, sensor, logic gate, enzyme and binding partner • Develop variation functions for site-specific mutagenesis and recombination • Fine-tune the parameters in the self-adaptive controller

  32. Sponsors iGEM community Teaching Affairs Office, USTC Graduate School, USTC Foreign Affairs Office, USTC School of Life Sciences, USTC Acknowledgments

  33. In Memory of Our Great Masters TsienHsue-shen(钱学森) 1911.12.11-2009.10.31 Father of Space Tech in China Bei Shi-zhang (贝时璋) 1903.10.10-2009.10.29 Father of Biophysics in China

  34. Thank You

  35. Supplementary Slides:

  36. Directed Evolution vs.Evolutionary Algorithm Poelwijk et al. 2007. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383-386.

  37. How to Design the Scoring Function? Here’s some examples.

  38. Transcription Repressor

  39. Transcription Repressor Evolution Object pCon x pX Scoring Function

  40. Transcription Repressor Evolution Object pCon x pX Scoring Function

  41. Transcription Repressor Evolution Object pCon x pX !Score Scoring Function

  42. Transcription Repressor Evolution Object X' = pCon - dX X pCon x !Score = pX KdX / (X + KdX) pX !Score Scoring Function

  43. Transcription Repressor Evolution Object X' = pCon - dX X pCon x !Score = pX KdX / (X + KdX) Xs = pCon / dX pX !Scores = pX dX KdX / (pCon + dX KdX) !Score Scoring Function

  44. Device (e.g. Sensor, Logic Gate):Supervised Learning Scoring Function Evolution Object Comparator Input Output !Score Supervision (Reference Output)

  45. More • For Enzyme: • If there is a sensor for the product or substrate, use it as the scoring function. • Else, perform evolution for a sensor first. • For Binding Partner: • E. coli two-hybrid systems. • Logic gates based on binding.

  46. How to Design the Scoring Function? Conclusion: GenotypePhenotypeSignal TransductionTranscription Rate (PoPS)Universal Interface of E.ADEM

  47. Control Strategiesin Evolutionary Algorithm • Deterministic Control • No Feedback • Adaptive Control • Feedback Control • Self-Adaptive Control • The Evolution of Evolution Parameters

  48. Variation Function • Targeted Mutagenesis • Activation Induced (Cytidine) Deaminase (AID) • iGEM 2008 Peking_University • iGEM 2008 Warsaw • Multiplex Automated Genome Engineering (MAGE) • Error-prone DNA polymerase I • Bacteriophage • Recombination • Site-specific recombination • Including Inversion, Excision/Integration and Translocation • Homologous recombination • Transposition

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