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ECOLITASTER: Cellular Biosensor

ECOLITASTER: Cellular Biosensor. Valencia iGEM 2006. Outline. Introduction Parts Design Systems Design Experimental work Conclusions. “To have success in science, you need some luck. Without it, I would never have become interested in genetics”. J.D. Watson. Introduction. Objectives:

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ECOLITASTER: Cellular Biosensor

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  1. ECOLITASTER: Cellular Biosensor Valencia iGEM 2006

  2. Outline • Introduction • Parts Design • Systems Design • Experimental work • Conclusions “To have success in science, you need some luck. Without it, I would never have become interested in genetics”. J.D. Watson.

  3. Introduction • Objectives: • Design a genetic system consisting on few genes that is able to give a graded response according to a concentration of an input. • Modular project. Different devices. • Use a biological mechanism to connect the membrane receptor with the genetic network, obtaining a cellular biosensor. • Use new synthetic parts.

  4. Project Design • This project is formed by two devices: a sensor and an actuator. • We use OmpR-P as input in order to assemble them. • We use a vanillin receptor (design in silico) as a sensor. • Our genetic circuit (actuator) is based on Weiss’ group work (Basu, Nature 2005) in order to obtain a graded response versus the concentration of a given input. • Incoherent circuit. • Semi-digital interpretation. Vanillin RFP & GFP

  5. Actuator Behavior

  6. Vanillin Receptor: mechanism E. coli PBP GN P V pdb 2DRI 271 res

  7. Parts Design • Promoters are critical elements designing those networks. • We focus our interest in binary promoters, i.e., promoters regulated by two transcription factors. • Integrating two signals. • Reduce the number of genes of the circuit. • Small size device. • Different implementations exhibiting logic behaviors, but not necessarily. • Computational protein design.

  8. Vanillin Receptor: DESIGNER methodology

  9. Systems Design • System and expected behavior. • Model and simulations. • Sensitivity analysis. • Robustness analysis. • Our biological system.

  10. System and Expected Behavior

  11. Model and Simulations • We use an effective model, modeling only protein concentrations: • We consider generic parts to make these simulations. Thus, we take common values for the parameters from the literature. However we expect a similar behavior:

  12. Sensitivity Analysis • The well working of the circuit depends on the promoters upstream of the two branches: pOmpR and pOmpRm.

  13. Robustness Analysis • We study the robustness of the gene circuit when there are oscillations in the sensing device. To perform that, we introduce a white noise in the input (OmpR-P). OmpR-P OmpR-P OmpR-P RFP RFP RFP GFP GFP GFP time time time

  14. Our Biological System

  15. Experimental Work • Parts construction. • Where are the parts? • Repositories. • E. coli genome. • Built from scratch. • Making our BioBricks. • pAND. • Vanillin receptor. • Fusion protein. • FACS results. • Our Registry.

  16. Where are the parts? (I) • Repositories: • pOmpR • pOmpRm • pLac • pTetR • GFP • RFP • TetR • cI • Tar-EnvZ

  17. Where are the parts? (II) • E. coli genome: • Trg • CRP

  18. Where are the parts? (III) • Built from scratch: • pAND • Vanillin PBP

  19. Making our BioBricks (I) XbaI • pAND: -93,5 -42 [Joung, Science 1994]

  20. Making our BioBricks (I) F0 F32 F71 5’ 3’ 3’ R91 R51 R16 R0 5’ XbaI • pAND: -93,5 -42 [Joung, Science 1994]

  21. Making our BioBricks (I) F0 F32 F71 5’ 3’ 3’ R91 R51 R16 R0 5’ DNA ligase 5’ 3’ 3’ 5’ XbaI • pAND: -93,5 -42 [Joung, Science 1994]

  22. Making our BioBricks (I) F0 F32 F71 5’ 3’ 3’ R91 R51 R16 R0 5’ DNA ligase 5’ 3’ 3’ 5’ PCR DNA polimerase & R91 + F71 5’ 3’ 3’ 5’ 5’ 3’ 5’ 3’ 3’ 5’ 3’ 5’ XbaI • pAND: -93,5 -42 [Joung, Science 1994]

  23. Making our BioBricks (II) • Vanillin receptor: aa sequence: KDTIALVVETLNKPDNVSLKDGAQKEADKLGYNLVVLDSQNNPAKELANVQDLTVRGTKILLIVPTDSDAVGNAVKMANQANIPVITLKRQATKGEVVSHIAADNVLGGKIAGDYIAKKAGEGAKVIELQGKAGTSAARELGEGFQQAVAAHKFNVLASQPADEDRIKGLNVMQNLLTAHPDVQAVFAQQDEMALGALRALQTAGKSDVMVVGDVGTPDGEKAVNDGKLAATIAELPDQIGAKGVETADKVLKGEKVQAKYPVDLKLVVKQ DESIGNER Computational design: Combinatory optimization $$ or €€ pBSKValencia

  24. Making our BioBricks (III) NdeI trg NdeI tar envZ • Fusion protein Trz. [Baumgartner, J. Bact. 1993]. • chemoreceptor Trg: periplasmic and transmembrane domains. • osmosensor EnvZ: cytoplasmic kinase/phosphatase domain.

  25. Making our BioBricks (III) NdeI trg Genomic PCR NdeI tar envZ BioBrick PCR • Fusion protein Trz. [Baumgartner, J. Bact. 1993]. • chemoreceptor Trg: periplasmic and transmembrane domains. • osmosensor EnvZ: cytoplasmic kinase/phosphatase domain.

  26. Making our BioBricks (III) NdeI trg Genomic PCR NdeI tar envZ BioBrick PCR • Fusion protein Trz. [Baumgartner, J. Bact. 1993]. • chemoreceptor Trg: periplasmic and transmembrane domains. • osmosensor EnvZ: cytoplasmic kinase/phosphatase domain. NdeI digestion NdeI digestion & dephosphorilation

  27. Making our BioBricks (III) NdeI trg Genomic PCR mix + ligate NdeI tar envZ BioBrick PCR trg NdeI envZ • Fusion protein Trz. [Baumgartner, J. Bact. 1993]. • chemoreceptor Trg: periplasmic and transmembrane domains. • osmosensor EnvZ: cytoplasmic kinase/phosphatase domain. NdeI digestion NdeI digestion & dephosphorilation

  28. Making our BioBricks (III) NdeI trg Genomic PCR mix + ligate NdeI tar envZ BioBrick PCR trg NdeI envZ • Fusion protein Trz. [Baumgartner, J. Bact. 1993]. • chemoreceptor Trg: periplasmic and transmembrane domains. • osmosensor EnvZ: cytoplasmic kinase/phosphatase domain. NdeI digestion NdeI digestion & dephosphorilation

  29. Making our BioBricks (IV) E X S P pTetR-RFP E X Trg-envZ S P • From wild type to BioBrick, a powerful screening method:

  30. Making our BioBricks (IV) E X S P EcoRI + PstI digestion & dephosphorilation pTetR-RFP E X Trg-envZ S P EcoRI + PstI digestion • From wild type to BioBrick, a powerful screening method:

  31. Making our BioBricks (IV) E X S P EcoRI + PstI digestion & dephosphorilation pTetR-RFP mix & ligate & transformation E X Trg-envZ S P EcoRI + PstI digestion • From wild type to BioBrick, a powerful screening method:

  32. Making our BioBricks (IV) E X S P EcoRI + PstI digestion & dephosphorilation pTetR-RFP mix & ligate & transformation E X Trg-envZ S P EcoRI + PstI digestion pTetR-RFP trg-envZ • From wild type to BioBrick, a powerful screening method:

  33. FACS results (I) • Promoter pOmpR with GFP as reporter: Set: pOmpR-RBS-GFP-T Negative control: XL1-Blue Positive control: Green fluorophore

  34. FACS results (II) • Characterization of pOmpR and pOmpRm. Set: pOmpR-RBS-GFP-T Set: pOmpR-RBS-GFP-T Negative control: XL1-Blue Positive control: Green fluorophore

  35. Our Registry • Parts submited by Valencia:

  36. Conclusions • We have designed a genetic system consisting on 7 genes, expected to give a graded response according to vanillin concentration. • We use the phosphorilation mechanism to connect the membrane receptor with the genetic network, obtaining a cellular biosensor. • Our use of a two-regulator promoter allows to integrate signals and reduce the number of genes required for a device. • Computational design of a PBP-vanillin receptor.

  37. Acknowledgements • EU FP6 NEST SYNBIOCOMM project (financial support). • Escuela Técnica Superior de Ingenieros Industriales (Universidad Politécnica de Valencia). • Instituto de Ciencia Molecular (Universitat de València). • E. O’Connor (FACS services). • A. Moya and A. Latorre (Cavanilles).

  38. UPV-UV Valencia iGEM 2006

  39. Our team

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