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Synthetic Biology Escherichia coli counter iGEM Summer 2004

Explore the innovative design of a synthetic biology counter system using Escherichia coli, developed in the iGEM Summer 2004 project led by Nathan Walsh. This design leverages the unique properties of E. coli to count inputs, record data in DNA, and offers versatile applications in various fields.

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Synthetic Biology Escherichia coli counter iGEM Summer 2004

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  1. Synthetic BiologyEscherichia coli counter iGEM Summer 2004 Nathan Walsh April 21, 2005

  2. Acknowledgments Harvard University • John Aach • Patrik D'haeseleer • Gary Gao • Jinkuk Kim • Xiaoxia Lin • Nathan Walsh • George Church Boston University • Will Blake • Jim Flanigon • Farren Isaacs • Ellen O’Shaughnessy • Neil Patel • Margot Schomp • Jim Collins Thanks to: Drew Endy & BioBricks community, MIT, Blue Heron and all others who have supported us along the way.

  3. Overview • Objectives & Design • Testing Components • Goals • Conclusions and Next Steps

  4. ObjectivesFeatures/Design Constraints • Ability to count identical inputs or sets of identical inputs. • Memory of the count recorded in the DNA of current counter (and progeny). • Modular bit design and linkage allows array of n-bits to count up to 2n • Exploit new class of natural mechanisms for use in synthetic biology.

  5. ObjectivesPotential Applications • Programmed cell death • Safety • Therapeutic dosage • Environmental diagnostic • Counting times pollution thresholds exceeded • Metabolic diagnostic • Count the number of times glucose levels exceeded

  6. DesignPhage Int/Xis system Phage attachment sites attP O P P’ O B B’ attB Bacterial attachment sites Int + Xis Int Integrated Left attachment sites attL Integrated Right attachment sites attR O O B P’ P B’ Stably integrated prophage

  7. DesignPhage Int/Xis system with inverted att sites Phage attachment sites attP Bacterial attachment sites attB* O O P P’ B B’ Int + Xis Int Integrated Right attachment site attR Integrated Left attachment site attL* O O P P’ B B’

  8. DesignIntegrase advantages • High fidelity – site specific and directional recombination (as opposed to homologous recombination) • Reversible – excision just as reliable as integration • Specific – each integrase recognize its own att sites, but no others • Numerous – over 300 known Tyr integrases and ~30 known Ser integrases • Efficient – very few other factors needed to integrate or excise • Extensively used – Phage systems well characterized and used extensively in genetic engineering (e.g., the GATEWAY cloning system by Invitrogen) Groth et al., Phage Integrases: Biology and Applications, J. Mol. Biol., 335: 667-678)

  9. Int2 Int2 Int2 Int2 Xis2 Xis2 Rpt1 Rpt1 int2 int2 xis2 xis2 rpt1 rpt1 int1 xis1 reporter2 attR2 – – attL2* term int2 xis2 reporter1 attR1–term– attL1* Int1 Xis1 Xis1 Rpt2 Rpt2 Int1 Int1 int1 xis1 xis1 rpt2 rpt2 int1 int1 int1 xis1 reporter2 int2 xis2 reporter1 attP2–term–attB2* attP1– – attB1* term DesignFull Cycle of Two ½-bits int2 int2 int2 xis2 reporter1 1 attR1–term– attL1* int1 xis1 reporter2 2 attP2–term– attB2*

  10. int2 int4 xis2 xis4 TF3 TF5 1 3 int3 int1 xis1 xis3 TF4 TF6 2 4 DesignChaining bits together

  11. ComponentsComposite half bits in BioBricks Two 2kb composite parts are currently being built by Blue Heron: λ Int+ LVA p22 attP Reverse Terminator p22 attB (rev comp) λXis +AAV ECFP +AAV λHalf Bit BBa_I11060 : BBa_I11020 BBa_I11033 BBa_B0025 BBa_I11032 BBa_I11021 BBa_E0024 p22 Int+ LVA λattP Terminator λattB (rev comp) P22 Xis +AAV EYFP +AAV p22 Half Bit BBa_I11061 : BBa_I11030 BBa_I11023 BBa_B0013 BBa_I11022 BBa_I11031 BBa_E0034 Lewis and Hatfull, Nuc. Acid Res., 2001, Vol. 29, 2205-2216 Andersen, Applied and Environmental Microbiology, 1998, 2240-2246

  12. ComponentsLutz and Bujard Vector

  13. TestingConstruct 1 - Overview PLlacO PLtetO Strain must make repressors BU has used dh5aZ1 before -laciq -> LacI -PN25 -> TetR -endogenous araC Xis Int T0 There are two sets of test plasmids, one for lambda and one for P22 origin Kan attB* attP GFP_AAV Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  14. TestingConstruct 1 – No GFP expression PLlacO PLtetO dh5aZ1 Xis No GFP expression: -Can’t continue after KanR -Can’t read through attP Int origin Kan attB* attP GFP_AAV Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  15. TestingTest Construct 2 – Might not be KanR problem Para-1 PLtetO dh5aZ1 attP Int GFP_AAV GFP is not inducible Likely problem is attP attB* origin Kan Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  16. TestingTest Construct 3 – GFP alone works Para-1 PLtetO dh5aZ1 GFP_AAV Int GFP is produced origin Kan Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  17. TestingGFP is produced in the cells

  18. TestingConstruct 1 – Possible explanations for failure PLlacO PLtetO dh5aZ1 Xis Int Can’t read through attP Cloning Problem near PLlacO in lambda construct (SalI) Beginning of Int and end of Xis overlap by 40 amino acids. End of Int and attP overlap. origin Kan attB* attP GFP_AAV Can’t continue after KanR Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  19. TestingTest Construct 1 – Fix PLlacO PLtetO dh5aZ1 Xis Other Issues: -Digests same size Int -Reclone l Integrase -Mutagenize attP site -Swap attP and attB -Have KanR-GFP intervening sequence be coding origin Kan attB* attP GFP_AAV -Reduce excess space Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  20. GoalFirst bit counter PLlacO PLtetR Lambda Int p22 Int p22 attB* Lambda attB* Lambda Xis p22 Xis Lambda attP GFP_AAV p22 attP Kan pSC101 Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210

  21. Questions for DiscussionPlease speak up with ideas! • Is there enough Int? • Do the PLlacO and PLtetO leak? • How can we measure levels of Int/Xis? • Does Int binding to att block read-through? • What other constructs would be useful?

  22. Synthesis and Testingdh5aZ1 – and why we need a new strain Try: OmniMAX2-T1 (invitrogen)

  23. How Gateway does it Gateway uses three methods • Promoter – attB1 – rbs – gene of interest – attB2 • Promoter – rbs – Fusion – attB1 – gene of interest – attB2 • Promoter – attB1 – rbs – gene of interest – attB2 – Fusion attB1 and attB2 can be read through with no stop codons but the ribosome binding site (Shine Delgarno) must be included after the attB1 if a native start is required

  24. What we need to change The Xis-attB-GFP junction We want to make a protein across the junction The GFP-attP-terminator We want the attP and a transcriptional terminator to follow the GFP The next slides show P22 than lambda

  25. P22Xis-P22attB-GFP junction PLtetO rbs xis attB rbs gfp attP* t0 rbs int* F--T--M--S--*--*-- M—R—K—G- --H--D--K--L--I--T--Q--R--I--R--N--A--K--V--V--K--E--A--A--Y--A--*-- ttcatgacaagctaataacgcagcgcattcgtaatgcgaaggtcgttaaggaggcagcctatgcgtaagga attB rbs PLtetO: Lambda phage promoter with tet operator sites acting as repressive elements rbs:Ribosome binding sites (Shine Delgarno) TAAGGAGG is complementary to 16S rRNA attB/attB1: Phage P22 attachment site in host (capital letters are the Gateway l attB1) xis: Phage P22 excisionase int*: 58 aa coding region to allow GFP in same operon. Corresponds to first 41 aa of Int.

  26. GFP-P22attP region PLtetO rbs xis attB rbs gfp attP’ t0 rbs int* A--*--*-- taataatttttggtacttctgtcccaaatatgtcccacagtaaaaataaggaaggcacgaataatacgt\ Aagtatttgatttaactggtgccgataataggagacgaacctacgaccttcgcattacgaattataagaact\ accttttaagtcaacaacataccacgtcatacctgcgctcacacgtcccatcttcgaaagacatgcaaagcc\ ttgcaaaccgatgcaaagatttgtatgtcccatttttgtcccaaaccacttag Terminator ggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacg\ ctctcctgagtaggacaaatccgcc attP: Phage integrase sites from phage P22 t0: Bacteriophage lambda transcriptional terminator

  27. lXis-lattB-GFP junction rbs PLtetO rbs l xis l attB1 gfp l attP1’ t0 rbs int* K--A--K--S--*--*-- M—R—K—G- -R--R--S--H—N—N—K—F—V—Q—K—S—R—L—R—R—Q—A--Y—A--* AAGGCGAAGTCAtaataACAAGTTTGTACAAAAAAGCAGGCTaaggaggcaggcctatgcgtaagga attB1 rbs PLtetO: Lambda phage promoter with tet operator sites acting as repressive elements rbs:Ribosome binding sites (Shine Delgarno) TAAGGAGG is complementary to 16S rRNA attB1: Phage l attachment site attB1 from Gateway (BOB’) xis: Phage P22 excisionase int*: 58 aa coding region to allow GFP in same operon. Corresponds to first 41 aa of Int.

  28. GFP-lattP region rbs PLtetO rbs l xis l attB1 gfp l attP1’ t0 rbs int* A--*--*-- taataacatagtgactggatatgttgtgttttacagtattatgtagtctgttttttatgcaaaatctaatt\ Taatatattgatatttatatcattttacgtttctcgttca(gcttttttgtacaaacttg)gcattataaaaaa\ gcattgctcatcaatttgttgcaacgaacaggtcactatcagtcaaaataaaatcattattt Terminator ggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacgct\ ctcctgagtaggacaaatccgcc attP: Phage integrase sites from phage l modified by Gateway (p’op) t0: Bacteriophage lambda transcriptional terminator

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  30. Sequential D Flip-flop Memory Element DNA top half bit Conditional Logic to assure only one signal is passed Sequential D Flip-flops using NOR gates with separate clocks Int+Xis IPTG Int alone Int Int alone Int TET Int+Xis Memory Element DNA bottom half bit Conditional Logic

  31. Circuits R-S flip-flop (NAND) R-S flip-flop (NOR) R R Q Q S S SR Latch Clocked R-S flip-flop (NOR) Clocked D flip-flop (NOR) R D Q Q CP CP S D Flip-flop Master Slave D flip-flop (NOR) T flip-flop (NOR) D Q Q CP CP Negative Edge Triggered Flip-flop

  32. Multi-University Collaboration Harvard University • John Aach • Farren Isaacs • Jinkuk Kim • Sasha Wait • Nathan Walsh • George Church Boston University • Ellen O’Shaughnessy • Margot Schomp • Jim Collins

  33. Simulation Purpose • To validate concept + alternatives, identify system sensitivities Implementation • Mixed ODE / stochastic model using MatLab Simulink • No uni-directional terminators Level of Detail • Pair of coupled half-bits • Int and Xis mRNAs and proteins • Half-bit DNA states • IPTG and tet pulses Parameters • Mixture of literature values + model derived estimates Results so far • Stable switching depends on stability of Int vs. Xis

  34. Simulation Results Pulses: IPTGTet Seconds 1st half bit DNA mRNA: Int-Xis Int Protein:Int-Xis Xis Int Seconds DNA 2nd half bit mRNA: Int-Xis Int Protein:Int-Xis Xis Int Seconds

  35. Simulation processing • Initial configuration IPTG half-bit 1 0 0 Int Xis tet Xis half-bit 2 0 0 Int = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  36. Xis protein I Int protein X Int-Xis mRNA IPTG half-bit 1 0 0 Int Xis Int-Xis I X tet Xis half-bit 2 0 0 I X Simulation processing • First IPTG pulse Int = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  37. Xis protein I Int protein X Int-Xis mRNA Int-Xis I X I X Simulation processing • First IPTG pulse IPTG half-bit 1 0 0 Int Xis tet half-bit 2 1 1 Xis Int = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  38. Simulation processing • Post first IPTG pulse IPTG half-bit 1 0 0 Int Xis tet half-bit 2 1 1 Xis Int = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  39. I X Int-Xis mRNA Xis protein Int-Xis I X Int protein I X Simulation processing • First tet pulse IPTG half-bit 1 0 0 Int Xis tet half-bit 2 1 1 Int Xis = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  40. I X Int-Xis mRNA Xis protein Int-Xis I X Int protein I X Simulation processing • First tet pulse IPTG Xis half-bit 1 1 1 Int tet half-bit 2 1 1 Int Xis = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  41. Simulation processing • Post first tet pulse IPTG Xis half-bit 1 1 1 Int tet half-bit 2 1 1 Int Xis = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  42. Int protein I I Int mRNA Simulation processing • Second IPTG pulse IPTG half-bit 1 Xis 1 1 Int tet half-bit 2 1 1 Int Xis = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  43. Int protein I I Int mRNA Simulation processing • Second IPTG pulse IPTG half-bit 1 Xis 1 1 Int tet Xis half-bit 2 0 0 Int = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  44. Simulation processing • Post second IPTG pulse IPTG half-bit 1 Xis 1 1 Int tet Xis half-bit 2 0 0 Int = integrated (attL / attR), requires Int+Xis to switch 0 = ‘excised’ (attP / attB), requires Int to switch 1

  45. Model ODEs: example of basic structure • mRNA ODEs: 0 order generation 1st order decay • Generation / decay rates expressed as functions of 70, RNAse concentrations, and doubling time • Generation depends on variable DNA that represents state of DNA Amount lost to cell division (mRNA) Amount Synthesized (DNA state) Amount Degraded (mRNAInt-Xis, RNAseH*) ∆mRNAInt-Xis= - -

  46. Model ODEs: additional details • mRNA and protein stored as numbers of molecules • Int, Xis protein ODEs include Int-Xis complexing as well as generation, decay, dilution • Effect of transcript lengths on transcription and translation taken into account via MatLab “transport delays” • Two sets of variables & equations one for each half-bit • 10 variables + 10 equations, not including DNA state variables • IPTG and tet: cycles of 4 parts of 1 hr 15min • exposure to IPTG, recovery, exposed to Tet, recovery

  47. Wrong!! State switching modeled by change in probability, not concentration where f(Int(t))t = probability of switch between t and t+t Stochastic Modeling vs. ODEs • DNA state switching not correctly modeled by rate equation

  48. Stochastic Modeling switching probability f(X) = 1-(1-P)X • P = probability of integration or excision in time unit / molecule • PInt = probability of integration / Int molecule • PInt-Xis = probability of excision / Int-Xis complex • X = number of molecules of Int or Int-Xis • Additional constraint: X > Xmin • Implementation • Pick random number U from uniform distribution 0..1 • If (X > Xmin) and U < f(X), invert DNA state

  49. Matlab “Counter” Specific Models Protease and RNAse levels are constant The ProtInt and ProtInt-Xis output from one half bit are inputs for other half bit The number of molecules are displayed on the “oscilliscopes”

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