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

2. Acknowledgments Boston University Will Blake Jim Flanigon Farren Isaacs Ellen O’Shaughnessy Neil Patel Margot Schomp Jim Collins Harvard University John Aach Patrik D'haeseleer Gary Gao Jinkuk Kim Xiaoxia Lin Nathan Walsh George Church 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. Objectives Features/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 2 n Exploit new class of natural mechanisms for use in synthetic biology.

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

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

8. Design Integrase 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. StatePulseProducts 0 0 1AInt 2 0 1 2A Int 1 Xis 1 Rpt 2 1 1 1B Int 2 Xis 2 Rpt 1 1 0 2BInt 1 0 0 Design Full Cycle of Two ½-bits 1 xis 2 reporter 1 int 2 2 xis 1 reporter 2 int 1 attR 1 –term– attL 1 * attP 2 –term– attB 2 * int 2 Int 2 int 2 Int 2 xis 1 reporter 2 int 1 attR 2 – – attL 2 * term int 1 Int 1 xis 1 Xis 1 rpt 2 Rpt 2 xis 1 Xis 1 rpt 2 Rpt 2 int 1 Int 1 attP 1 – – attB 1 * xis 2 reporter 1 int 2 term int 2 Int 2 xis 2 Xis 2 rpt 1 Rpt 1 xis 1 reporter 2 int 1 attP 2 –term– attB 2 * int 2 Int 2 xis 2 Xis 2 rpt 1 Rpt 1 int 1 Int 1 xis 2 reporter 1 int 2 attR 1 –term– attL 1 *

10. 1 xis 2 TF 3 int 2 Design Chaining bits together 2 xis 1 TF 4 int 1 3 xis 4 TF 5 int 4 4 xis 3 TF 6 int 3

11. Components Composite half bits in BioBricks λ Xis +AAV ECFP +AAV λ Int+ LVA BBa_E0024BBa_I11020BBa_I11021 p22 attP BBa_I11033 Reverse Terminator BBa_B0025 p22 attB (rev comp) BBa_I1103BBa_I11032BBa_I11060BBa_I11060 : P22 Xis +AAV EYFP +AAV p22 Int+ LVA BBa_E0034BBa_I11030BBa_I11031 λ attP BBa_I11023 Terminator BBa_B0013 λ attB (rev comp) BBa_I11022BBa_I11061BBa_I11061 : Lewis and Hatfull, Nuc. Acid Res., 2001, Vol. 29, 2205-2216 Andersen, Applied and Environmental Microbiology, 1998, 2240-2246 Two 2kb composite parts are currently being built by Blue Heron: λ Half Bit p22 Half Bit

12. Components Lutz and Bujard Vector

13. Testing Construct 1 - Overview Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210 Xis Int PLlacOPLtetO GFP_AAV attP attB* origin Kan Strain must make repressors BU has used dh5  Z1 before -laciq -> LacI -PN25 -> TetR -endogenous araC There are two sets of test plasmids, one for lambda and one for P22 T0

14. Testing Construct 1 – No GFP expression Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210 Xis Int PLlacOPLtetO GFP_AAV attP attB* origin Kan dh5  Z1 No GFP expression: -Can’t continue after KanR -Can’t read through attP

15. Testing Test Construct 2 – Might not be KanR problem Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210 Int Para-1PLtetO GFP_AAV attP attB* origin Kan dh5  Z1 GFP is not inducible Likely problem is attP

16. Testing Test Construct 3 – GFP alone works Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210 Int Para-1PLtetO GFP_AAV origin Kan dh5  Z1 GFP is produced

17. Testing GFP is produced in the cells

18. Testing Construct 1 – Possible explanations for failure Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210 Xis Int PLlacOPLtetO GFP_AAV attP attB* origin Kan dh5  Z1 Can’t read through attP Beginning of Int and end of Xis overlap by 40 amino acids. End of Int and attP overlap. Can’t continue after KanR Cloning Problem near PLlacO in lambda construct (SalI)

19. Testing Test Construct 1 – Fix Lutz and Bujard, Nuc. Acids Res., 1997, Vol. 25, No. 6 1203-1210 Xis Int PLlacOPLtetO attP attB* origin Kan dh5  Z1 GFP_AAV Other Issues: -Digests same size -Swap attP and attB -Have KanR-GFP intervening sequence be coding -Mutagenize attP site -Reclone Integrase -Reduce excess space

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

21. Questions for Discussion Please 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 Testing dh5  Z1 – 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 xisattBrbsgfpattP*rbsPLtetO rbsint* 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 attBrbs t0 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 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 xisattBrbsgfpattP’rbsPLtetO rbsint* t0 A--*--*-- taataatttttggtacttctgtcccaaatatgtcccacagtaaaaataaggaaggcacgaataatacgt\ Aagtatttgatttaactggtgccgataataggagacgaacctacgaccttcgcattacgaattataagaact\ accttttaagtcaacaacataccacgtcatacctgcgctcacacgtcccatcttcgaaagacatgcaaagcc\ ttgcaaaccgatgcaaagatttgtatgtcccatttttgtcccaaaccacttag Terminator ggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacg\ ctctcctgagtaggacaaatccgcc attP: Phage integrase sites from phage P22 t0: Bacteriophage lambda transcriptional terminator

27. Xis- attB-GFP junction xis attB1 rbs gfp attP1’rbsPLtetO rbsint* 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 attB1rbs t0 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 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- attP region xis attB1 rbs gfp attP1’rbsPLtetO rbsint* t0 A--*--*-- taataacatagtgactggatatgttgtgttttacagtattatgtagtctgttttttatgcaaaatctaatt\ Taatatattgatatttatatcattttacgtttctcgttca(gcttttttgtacaaacttg)gcattataaaaaa\ gcattgctcatcaatttgttgcaacgaacaggtcactatcagtcaaaataaaatcattattt Terminator ggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacgct\ ctcctgagtaggacaaatccgcc attP: Phage integrase sites from phage modified by Gateway (p’op) t0: Bacteriophage lambda transcriptional terminator

29. 0

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

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

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

34. 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

35. Simulation Results Tet Pulses: IPTG Tet DNA mRNA: Int-Xis Int Protein:Int-Xis Xis Int mRNA: Int-Xis Int Protein:Int-Xis Xis Int 2 nd half bit 1 st half bit Seconds

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

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

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

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

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

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

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

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

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

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

46. Model ODEs: example of basic structure mRNA ODEs: 0 order generation 1 st 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 ∆mRNA Int-Xis = Amount Synthesized (DNA state) Amount Degraded (mRNA Int-Xis, RNAseH*) -- Amount lost to cell division (mRNA)

47. 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

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

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

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

51. Matlab: Molecular Biology Models mRNA protein

52. Matlab Molecular Biology Models Complex between protein A and protein B

53. Matlab “Counter” Specific Models Each half bit combines the switching function, the mRNA, and the protein. The DNA state of each half bit is maintained as a global variable.

54. Matlab “Counter” Specific Models The two half bits differ in that when they are in the integrated state one makes mRNA Int and the other make mRNA Int-Xis.

55. Simulation Results – revisited Tet Pulses: IPTG Tet DNA mRNA: Int-Xis Int Protein:Int-Xis Xis Int mRNA: Int-Xis Int Protein:Int-Xis Xis Int 2 nd half bit 1 st half bit Seconds

56. Int/Xis degradation rates The simulation is sensitive to the relative degradation rates of Int and Xis. Previously Int was less stable, but in this simulation the stabilities are equal.

57. Simulation Next steps and directions Continue evaluation of design elements –Explore more of parameter space –DNA element copy number –Reversible terminators –Single combined bits vs. coupled half-bits –Link multiple bits Incorporate more biology –Continue refining parameters based on research –Add additional molecules RNA polymerase, Ribosomes, competing DNA and RNA –Model cell volume changes –Model excision via Int / Xis / DNA interactions, not Int+Xis complex

58. Considerations Phage systems –Selection, P22, HK022, P21 to start research + experiment to extend –Cross-reactivity –Multiple independent attP/attB per integrase E. coli strains –Natural phage attB sites –Recombination (use RecA-) Copy number –F-plasmid? Speed of response –Riboregulators? Gateway System intellectual property?

59. Conclusions Next Steps Conclusions Phage integrase systems useful for synthetic biology Integrase used to meet design objectives: –DNA memory, counts same inputs, chainable Components are currently being constructed and tested ODE / stochastic simulator Next Steps Continue with construction, testing of components Continue evaluating and refining designs with simulator Research, experimentation, and modifications to address considerations

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

61. Design Bit counter initial concept Counting mechanism: –Initial state: 0 0 0 –Pulse 1: 1 0 0 –Pulse 2: 0 1 0 –etc.... Race condition problems between each Int and Xis Int 1 0 0 Xis 1 Int 2 Xis 2 Int 2 Xis 3 11 0 0 0 0 0 1

62. Design First Steps XisTF4 XisTF3 Int XisTF5 XisTF6 Int 1 2 3 4 Riboswitch counter   Integrase bit counter Cell-cycle counter  01100110 123123

63. Definition Finite state machine A model of computation consisting of a set of states, a start state, an input alphabet, and a transition function that maps input symbols and current states to a next state.model of computation statesstart statealphabet transition functionnext state -National Institute of Standards and Technology