SUPPORTING INFORMATION

Transcription

1 SUPPORTING INFORMATION Cell-free translation is more variable than transcription Fabio Chizzolini, Michele Forlin, Noël Yeh Martín, Giuliano Berloffa, Dario Cecchi, and Sheref S. Mansy Deposited DNA sequences Linear DNA sequences and plasmid templates employed for the reported experiments were deposited in the ACS Synthetic Biology Registry, Inventory of Composable Elements (ICE) ( Table S1. List of designed E. coli ribosome binding sites. Name Sequence* RL055A TAAGGAGAA taatct ATG CD104 CD105 CD106 CD107 CD108 CD109 CD110 CD114 CD116 CD117 CD118 CD119 CD120 CD123 CD125 CD126 CD127 CGGAAAGGT taatct ATG CGGAGAGGT taatct ATG CGGGGAGGT taatct ATG TAGAAGAAC taatct ATG TAAGGAAAC taatct ATG CGAGGAAAC taatct ATG CAAGGAGAC taatct ATG TAATTCTTG taatct ATG TAAGGATTG taatct ATG GCCTGCTTG taatct ATG TAAGGAGGG taatct ATG TAAGGAGGT taatct ATG CGGAGAGGC taatct ATG TAAGGAGAA taatct TTG TAAGGAGGA taatct ATG TAAGGAGAT taatct ATG GAAGGAGAT atacat ATG *Each ribosome binding site is shown in capital letters with bold font signifying the bases that diverge from the reference sequence, RL055A. 1 The start codon ATG follows after a short spacer. Sequences from CD104 to CD120 are the 13 statistically designed ribosome binding sites. CD123 has, instead of the more common start codon ATG, the rarer start codon TTG. CD127 contains the standard pet21b ribosome binding site. 1

2 Table S2. Different 3 -ends used in this study. name 3 -end of the PCR product template TAA TAA+56bp TAA+Spinach TAA+91bp TAA+longTϕTerm TAA+Spinach+Tϕ Term TAA+TϕTerm TAA TAATCGAGCACCACCACCACCACCACTGAGATCCGGCTGCTAACAAAGCCCGAAAGGAA TAAGCCCGGATAGCTCAGTCGGTAGAGCAGCGGCCGGACGCAACTGAATGAAATGGTGA AGGACGGGTCCAGGTGTGGCTGCTTCGGCAGTGCAGCTTGTTGAGTAGAGTGTGAGCTC CGTAACTAGTCGCGTCCGGCCGCGGGTCCAGGGTTCAAGTCCCTGTTCGGGCGCCA TAACTCGAGCACCACCACCACCACCACTGAGATCCGGCTGCTAACAAAGCCCGAAAGGA AGCTGAGTTGGCTGCTGCCACCGCTGAGCAATAAC TAACTCGAGCACCACCACCACCACCACTGAGATCCGGCTGCTAACAAAGCCCGAAAGGA AGCTGAGTTGGCTGCTGCCACCGCTGAGCAATAACTAGCATAACCCCTTGGGGCCTCTA AACGGGTCTTGAGGGGTTTTTTGCTGAAAGGAGGAACTATATCCGGATTGGCGAATGGG A TAAGCCCGGATAGCTCAGTCGGTAGAGCAGCGGCCGGACGCAACTGAATGAAATGGTGA AGGACGGGTCCAGGTGTGGCTGCTTCGGCAGTGCAGCTTGTTGAGTAGAGTGTGAGCTC CGTAACTAGTCGCGTCCGGCCGCGGGTCCAGGGTTCAAGTCCCTGTTCGGGCGCCATAG CATAACCCCTTGGGGCCTCTAAACGGGTCTTGAGGGGTTTTTTGCTCGAGCACCACCAC CACCACCACTGAGATCTGCTAACAAAGCCCGAAAGGAAGCTGAGTTGGCTGCTGCCACC GCTGAGCAATAACTAGCATAACCCCTTGGGGCCTCTAAACGGGTCTTGAGGGGTTTTTT GCTGAAAGGAGGAACT TAACTCGAGCACCACCACCACCACCACTGAGATCCGGCTGCTAACAAAGCCCGAAAGGA AGCTGAGTTGGCTGCTGCCACCGCTGAGCAATAACTAGCATAACCCCTTGGGGCCTCTA AACGGGTCTTGAGGGGTTTTTTGCTGAAAGGAGGAACT Table S3. Leader sequences tested to probe their influence on transcription and translation.* Leader sequence LS1 LS2 Sequence GGGAGATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAA GGAGATATACATATG GGGAGAGCGGATCCGAATTCAATTAGTTTGAACTTATAAGGAGAATAATCTATG LS3 LS4 LS5 GGGAGAATAATCATATTAGAATGCTTTAAGAAGGAGATATACATATG GGGAGATCTAAGTTTTTCCACTTGGTTTAAGAAGGAGATATACATATG GGGAGAGGTATAAAAAGCAAATACTAGGGGGGTAGAGAATG *Leader sequences LS1 and LS2 were taken from previously characterized genetic constructs. 1,2 Leader sequences LS3 and LS4 were randomized sequences, and LS5 was computationally designed employing the RBS calculator. 3 The ribosome binding site, underlined, was constant among the different leader sequences tested except for LS5. Each sequence ends with an ATG start codon. Table S4. List of T7 transcriptional promoters and ribosome binding sites combined together to control transcription and translation. Promoter name* DNA sequence Promoter Strength RBS name DNA sequence RBS Strength FC074 CCGGTTAATACGACTCACTATA Strong CD127 GAAGGAGAT Strong FC115 CCGGTTATTACGACTCACTAAA Mid CD110 CAAGGAGAC Mid FC108 CCGGTTAATACGTCTCACTATA Weak CD109 CGAGGAAAC Weak FC109 CCGGTCAACCCGACTCACTATA Very weak CD105 *These T7 transcriptional promoters were previously characterized. 2 CGGAGAGGT Very weak 2

3 Table S5. List of combinations of different promoters and ribosome binding sites used in Figure 2 and Figure S6. Sample number Promoter* RBS* 1 Strong Strong 2 Strong Mid 3 Strong Weak 4 Strong Very Weak 5 Mid Strong 6 Mid Mid 7 Mid Weak 8 Mid Very Weak 9 Weak Strong 10 Weak Mid 11 Weak Weak 12 Weak Very Weak 13 Very Weak Strong 14 Very Weak Mid 15 Very Weak Weak 16 Very Weak Very Weak * Strong, Mid, Weak, and Very Weak refer to sequences reported in Table S4. 3

4 Table S6. Protein expression kinetic model, reactions and associated parameters. ID Reactions Parameters Related RFP GFP BFP Part 1 DNA + T7RNAP = DNA_T7_complex r dna-t7_c= 97 µl/(pmol*min); r dna-t7_dc= 11.5 min -1 FC074 DNA + T7RNAP = DNA_T7_complex r dna-t7_c= 3.4 µl/(pmol*min); r dna-t7_dc= 0 min -1 FC108 DNA + T7RNAP = DNA_T7_complex r dna-t7_c= 0.08 µl/(pmol*min); r dna-t7_dc= 0 min -1 FC109 2 DNA_T7_complex + R1 -> DNA + T7RNAP + RNA r rna_prod= 0.14 µl/(pmol*min) r rna_prod= µl/(pmol*min) r rna_prod= 0.17 µl/(pmol*min) 3 RNA + Ribosome = r rna-rib_c= 0.11 µl/(pmol*min); r rna-rib_dc= 1.34 min -1 CD127 RNA_Rib_complex RNA + Ribosome = r rna-rib_c= 0.06 µl/(pmol*min); r rna-rib_dc= 1.02 min -1 CD110 RNA_Rib_complex RNA + Ribosome = RNA_Rib_complex r rna-rib_c= µl/(pmol*min); r rna-rib_dc= 0.0 min -1 CD109 4 RNA_Rib_complex + R2 -> RNA + Ribosome + P r prot_prod= 5 µl/(pmol*min) r prot_prod= 0.44 µl/(pmol*min) r prot_prod= 0.03 µl/(pmol*min) 5 P -> MatureP r prot_mat= min -1 r prot_mat= 5 min -1 r prot_mat= 5 min - 6 T7RNAP -> r deg_t7= 0.02 min -1 7 R1 -> r deg_r1= min -1 8 RNA -> r deg_rna= min -1 9 Ribosome -> r deg_rib= min R2 -> r deg_r2= 0 min -1 1 Table S7. Protein expression kinetic model, initial species concentrations. Species Initial Concentration (pmol/µl) DNA * sbvar T7RNAP * mbvar DNA_T7_complex 0 R1 R2 19 * mbvar 2.92 * mbvar RNA 0 Ribosome 2.4 * mbvar RNA_Rib_complex 0 Protein 0 Matured Protein 0 Table S8. Protein expression kinetic model, noise parameter distributions. Noise Parameters sbvar mbvar N(µ=1, σ=0.025) N(µ=1, σ=0.05) 4

5 Table S9. Cascade protein expression model, reactions and parameters. ID Reactions Parameters 1 DNA1 + T7RNAP = DNA1_T7_complex r dna1-t7_c r dna1-t7_dc 2 DNA_T7_complex + R1 -> DNA1 + T7RNAP + RNA1 r rna1_prod 3 RNA1 + Ribosome = RNA1_Rib_complex r rna1-rib_c r rna1-rib_dc 4 RNA1_Rib_complex + R2 -> RNA1 + Ribosome + T3RNAP r prot1_prod 5 DNA2 + T3RNAP = DNA2_T3_complex r dna2-t3_c r dna2-t3_dc 6 DNA2_T3_complex + R1 -> DNA2 + T3RNAP + RNA2 r rna2_prod 7 RNA2 + Ribosome = RNA2_Rib_complex r rna2-rib_c r rna2-rib_dc 8 RNA2_Rib_complex + R2 -> RNA2 + Ribosome + RFP r RFP_prod 9 RFP -> MatureRFP r RFP_mat 10 T7RNAP -> r deg_t7 11 T3RNAP -> r deg_t3 12 R1 -> r deg_r1 13 RNA -> r deg_rna1 14 RNA2 -> r deg_rna2 15 Ribosome -> r deg_rib 16 R2 -> r deg_r2 Table S10. Cascade protein expression model, initial species concentrations. Species Initial Concentration (pmol/µl) DNA1 DNA2 T7RNAP T3RNAP 0 DNA1_T7_complex 0 DNA2_T3_complex 0 R1 R2 RNA1 0 RNA2 0 Ribosome RNA1_Rib_complex 0 RNA2_Rib_complex 0 RFP 0 MatureRFP * sbvar * sbvar * mbvar 19 * mbvar 2.92 * mbvar 2.4 * mbvar 5

6 Table S11. Cascade protein expression model, noise parameter distributions. Noise Parameters* sbvar1 N(µ=1, σ=0.025) sbvar2 N(µ=1, σ=0.025) mbvar N(µ=1, σ=0.05) * sbvar1 and sbvar2 are considered equal when the cascade is built with a single piece of DNA. Table S12. Genetic parts employed for each of the pixels composing the target RGB triangle. Pixel number Promoter RFP* RBS RFP* Promoter GFP* RBS GFP* Promoter BFP* RBS BFP* 1 Strong Strong Weak Weak Weak Weak 2 Strong Strong Weak Mid Weak Weak 3 Strong Mid Mid Mid Weak Weak 4 Mid Strong Mid Strong Weak Weak 5 Mid Strong Strong Mid Weak Weak 6 Mid Mid Strong Strong Weak Weak 7 Weak Weak Strong Strong Weak Weak 8 Strong Mid Weak Mid Mid Mid 9 Strong Mid Mid Mid Mid Mid 10 Mid Mid Mid Mid Mid Mid 11 Mid Mid Strong Mid Mid Mid 12 Weak Strong Strong Mid Mid Mid 13 Strong Mid Weak Weak Mid Strong 14 Mid Strong Mid Mid Strong Mid 15 Mid Strong Mid Mid Mid Mid 16 Mid Mid Strong Mid Mid Mid 17 Weak Weak Strong Mid Mid Mid 18 Strong Mid Weak Mid Mid Mid 19 Strong Mid Mid Mid Mid Strong 20 Mid Strong Mid Strong Strong Strong 21 Strong Mid Weak Weak Strong Mid 22 Mid Strong Mid Mid Strong Mid 23 Weak Weak Mid Mid Strong Mid 24 Mid Mid Weak Mid Strong Strong 25 Weak Weak Weak Weak Strong Strong *Strong, Mid, and Weak refer to transcriptional promoters and RBS sequences as indicated in Table S4. These genetic parts were used to construct the image in Figure S14. 6

7 Table S13. Different positions in which the EsaR operator was placed downstream of a T7 transcriptional promoter. Promoter name T7 promoter +1 Spacer EsaR operator Promoter 1 CCGGTTAATACGACTCACTATA G GGAGATTGTGAGCGG CCTGTACTATAGTGCAGGT Promoter 2 CCGGTTAATACGACTCACTATA G GGAG CCTGTACTATAGTGCAGGT Promoter 3 CCGGTTAATACGACTCACTATA G CCTGTACTATAGTGCAGGT Table S14. Repressor protein expression model, reactions and parameters (inferred parameters in parenthesis). ID Reactions Parameters 1 DNA1 + T7RNAP = DNA1_T7_complex r dna1-t7_c r dna1-t7_dc 2 DNA_T7_complex + R1 -> DNA1 + T7RNAP + RNA1 r rna1_prod 3 RNA1 + Ribosome = RNA1_Rib_complex r rna1-rib_c r rna1-rib_dc 4 RNA1_Rib_complex + R2 -> RNA1 + Ribosome + EsaR r prot1_prod 5 DNA2 + T7RNAP = DNA2_T7_complex r dna2-t7_c r dna2-t7_dc 6 DNA2_T7_complex + R1 -> DNA2 + T7RNAP + RNA2 r rna2_prod 7 RNA2 + Ribosome = RNA2_Rib_complex r rna2-rib_c r rna2-rib_dc 8 RNA2_Rib_complex + R2 -> RNA2 + Ribosome + RFP r RFP_prod 9 RFP -> MatureRFP r RFP_mat 10 DNA2 + EsaR -> Inactive_DNA2 r EsaR_repr = 27.4 ul/pmol*min 11 EsaR + AHL = Inactive_EsaR r AHL_bind = 2.93 ul/pmol*min, r AHL_unbind = 27.4 min Inactive_DNA2 + AHL -> DNA2 + Inactive_EsaR r AHL_bind 13 T7RNAP -> r deg_t7 14 R1 -> r deg_r1 15 RNA -> r deg_rna1 16 RNA2 -> r deg_rna2 17 Ribosome -> r deg_rib 18 R2 -> r deg_r2 7

8 Table S15. Repression protein expression model, initial species concentrations. Species Initial Concentration (pmol/µl) AHL 0 DNA1 DNA2 T7RNAP DNA1_T7_complex 0 DNA2_T7_complex 0 R1 R2 RNA1 0 RNA2 0 Ribosome RNA1_Rib_complex 0 RNA2_Rib_complex 0 RFP 0 MatureRFP 0 EsaR 0 Inactive_EsaR 0 Inactive_DNA * sbvar * sbvar * mbvar 19 * mbvar 2.92 * mbvar 2.4 * mbvar Table S16. Repressor protein expression model, noise parameter distributions. Noise Parameters sbvar N(µ=1, σ=0.025) mbvar N(µ=1, σ=0.05) 8

9 Table S17. Parts employed for the repressor picture and RNA and protein predictions from the kinetic model.* Pixel # Promoter 1 RBS 1 Promoter 2 RBS 2 RNA [μm] OFF RNA [μm] ON RFP [μm] OFF RFP [μm] ON 1 Mid Strong Strong Strong Strong Weak Weak Weak Mid Strong Strong Mid Mid Strong Mid Mid Mid Mid Mid Mid Strong Strong Strong Strong Strong Strong Mid Mid Strong Strong Weak Weak *Promoter 1 and RBS 1 control expression of RFP and Spinach, while the repressor EsaR is under the control of Promoter 2 and RBS 2. The pixel numbers refer to the pixels shown in Supplementary Figure S19. Figure S1. Single gene characterization of ribosome binding sites. Ribosome binding sites (sequences in Table S1) were characterized by the expression of RFP (red bars) and GFP (green bars) with the PURE system for 6 h at 37 C. Linear, PCR product DNA templates carrying the different ribosome binding sites were generated following the sequences FC001 (GFP) and FC002 (RFP). 9

10 Figure S2. The relationship between the number of bases that show a perfect match with the ribosome binding site consensus sequence and gene expression. Correlations with RFP (a) and for GFP (b) are shown. The position of the matching bases is also taken into account. The data plotted are the same as in Supplementary Figure S1. Figure S3. The role of the 3 -UTR when using single-gene templates encoding either the fluorescent protein RFP or GFP in PURE system reactions. When the mrna ends right at the stop codon, gene expression is severely impaired. An additional 56 base sequence can partially rescue expression. However, a 91 unstructured region or structured regions containing a transcriptional terminator or the Spinach aptamer are able to fully recover gene expression. Data are the fluorescence after 6 h of expression with the PURE system at 37 C. Details on the 3 -UTRs employed can be found in Table S2, while full sequences from FC003 to FC015 plus FC033 are in the Inventory of Composable Elements (ICE). 10

11 Figure S4. The role of the 3 -UTR of the mrna in two-gene operons. Genetic constructs with the second gene encoding GFP (a) and RFP (b) were tested. The 3 -UTR only influenced the expression of the second gene. Data are the fluorescence after 6 h of expression with the PURE system at 37 C. The genetic sequences were FC016 to FC019, available in the Inventory of Composable Elements (ICE). Figure S5. Expression of RFP with different leader sequences. Different leader sequences (LS) (Table S3) affect translation (a) and transcription (b). Three out of the five tested leader sequences lead to similar expression of RFP. Reactions were at 37 C for 6 h with the PURE system. Details of the sequences can be found from FC020 to FC024, available in the Inventory of Composable Elements (ICE). 11

12 Figure S6. Control of single-gene expression with a combination of different promoters and ribosome binding sites. Four T7 transcriptional promoters were combined with four ribosome binding sites (Table S4). The resulting sixteen combinations (Table S5) were then tested for both transcription and translation using two different fluorescent reporter proteins: RFP (a), and BFP (b). Reactions were at 37 C for 6 h with the PURE system. Templates were generated by modifying promoters and ribosome binding sites starting from either pfc013a (RFP) or pdc051a (BFP). 12

13 Figure S7. The effect of different combinations of T7 transcriptional promoters and ribosome binding sites on the expression of fluorescent proteins from single-gene constructs. The leader sequence employed was LS1 (Table S3), while at the 3 -end of the mrna the Spinach aptamer followed the Tϕ terminator (Table S2). Strong, Mid, Weak, and Very weak refer to transcriptional promoters and RBS sequences as indicated (Table S4). Data are the fluorescence after 6 h of expression with the PURE system at 37 C. Sequences were generated starting from either pfc013a (RFP), pdc051a (BFP), or pcd100a (GFP). Figure S8. Variability of RNA and protein levels in an E. coli cell-free extract. Plotted data represents the coefficient of variation of RNA and RFP (white and black bars respectively) for both T7 and ptac promoters for reactions run on the same day with the same batch of cell extract. The coefficient of variation was calculated for transcription after 1 h (the point of maximal RNA) and for translation after 7 h. 13

14 Figure S9. Yin-Yang pictures were built with PURE system reactions placed in a 1538-well plate to confirm the variability of transcription and translation. The pictures were visualized by the fluorescence of expressed RNA (via the Spinach aptamer) and protein (via RFP). The DNA templates of black (strong transcriptional promoter, strong RBS), white (very weak transcriptional promoter, mid RBS), light grey (weak transcriptional promoter, strong RBS), and dark grey (mid transcriptional promoter, mid RBS) pixels were derivatives of pfc013a. Sequences are listed in Table S4. 14

15 Figure S10. A graphical representation of the model used to predict gene expression in PURE system reactions. Upon interacting with the template DNA, the T7 RNA polymerase can either transcribe RNA or detach from the DNA. Upon transcribing RNA, resources R1 are consumed until transcription halts. The ribosomes interact with the transcribed RNA, either by translating the RNA into protein or by detaching from the RNA. During translation, resources R2 are consumed until translation is no longer sustainable. Figure S11. Protein expression kinetic model fitting. Parameter estimation for the kinetic model identified an optimal set of parameters that fit well the experimental evidence, both for RNA and protein concentrations for the three fluorescent proteins. 15

16 Figure S12. The impact of photobleaching on the fluorescent output of the three different fluorescent proteins used in this study. Fluorescence emission was collected from the three different fluorescent proteins, either after being excited solely with the proper excitation wavelength for each specific fluorescent protein (white bars) (377 nm for BFP, 579 nm for RFP, 474 nm for GFP), or with three subsequent excitations (black bars) (377 nm, followed by 579 nm, followed by 474 nm). Figure S13. Protein expression predictions from the cascade circuit model. RFP expression distributions using different transcriptional promoters and ribosome binding sites in a cascading circuit. For highest expression, the model results suggest to avoid using a strong T7 promoter for the transcription of T3 RNAP and a medium or weak ribosome binding site for the translation of RFP. 16

17 Figure S14. The target, genetically encoded RGB picture. Each of the pixels composing the picture are labelled, and for each pixel a target intensity for the three colors (red, green and blue) is defined. The computational model was then used to predict which genetic parts should be employed for the expression of each of the three fluorescent proteins (RFP, GFP and BFP). The composition of the genetic construct for each pixel can be found in Table S12. Sequences were generated starting from either pfc013a (RFP), pdc051a (BFP), or pcd100a (GFP). 17

18 Figure S15. Comparison of four Yin-Yang pictures built with different genetic architectures of the cascade circuit. Four different combinations of promoters and ribosome binding sites were used to generate the picture. In the black pixels the expression of T3 RNA polymerase was controlled by a weak promoter and a medium strength ribosome binding site, while the expression of RFP and Spinach was under the control of a standard T3 promoter and a strong ribosome binding site. In the dark gray pixels, the same setup was used for the expression of T3 RNA polymerase, while the expression of RFP was reduced by employing a medium strength ribosome binding site. Light gray pixels reduced the expression of T3 RNA polymerase by using a weak ribosome binding site, therefore also reducing the expression of Spinach. RFP expression was controlled by a strong ribosome binding site. Finally, for the white pixels, a weak promoter and a middle strength ribosome binding site controlled the expression of T3 RNA polymerase, therefore reducing even more Spinach expression. RFP was under the control of a weak ribosome binding site. In the divided constructs picture, RFP and Spinach were not on the same piece of DNA that encoded T3 RNA polymerase. Conversely, the single construct pictures were made with genes residing on the same piece of DNA. Reactions were incubated with the appropriate genetic circuits for 6 h at 37 C after which the samples were moved into the wells of a 1536-well plate for visualization with a fluorescence imager. The sequences on two separate pieces of DNA were generated from FC025 and FC026. The sequences of the two genes on the same piece of DNA was from FC

19 Figure S16. The performance of a genetic cascade is significantly influenced by the architecture employed. Data from three independent experiments were averaged and plotted for the transcription and translation of the reporter gene encoding RFP and Spinach. (a) Transcription was affected by changes to the genetic architecture. Two genes on a single DNA, as opposed to two different DNA molecules, reduced transcriptional variability across three independent experiments. (b) Translation was greatly affected by the genetic architecture. Having the two genes within the same construct greatly reduced variability. Data are the fluorescence after 6 h of expression with the PURE system at 37 C. A schematic of the genetic constructs can be found in Figure 2e. Strong, Mid, and Weak refer to transcriptional promoters and RBS sequences as indicated Table S4. Promoter 1 and RBS 1 control the expression of T3 RNA polymerase, while RFP and Spinach are under the control of the standard T3 transcriptional promoter (5 -CCGGTAATTAACCCTCACTAAAGGGAGA-3 ) and RBS 2. The sequences were generated in the case of the genes on two separate pieces of DNA starting from FC025 and FC026, while in the case of the two genes on the same piece of DNA from FC

20 Figure S17. Repression of gene expression increases as the EsaR operator is closer to the transcriptional start site. The EsaR operator was placed in three different positions (Table S13). Transcription (a) and translation (b) were monitored by the fluorescence of the Spinach aptamer and mrfp1, respectively, with and without the co-expression of the transcriptional repressor EsaR on a separate piece of DNA. Reactions were incubated at 37 C with the PURE system for 6 h. The sequences to control the expression of the reporter gene were FC028, FC029 and FC030, while the repressor EsaR was under the control of the sequence FC

21 Figure S18. The effect of different transcriptional promoters and RBS sequences on the performance of repressor constructs. Expression of RNA, based on the fluorescence of the Spinach aptamer, (a) and protein, based on the expression of RFP, (b) is shown. Reactions were either in the presence or absence of 5 μm of 3-oxohexanoyl-homoserine lactone. Data are the fluorescence after 6 h of expression with the PURE system at 37 C. A schematic of the genetic construct can be found in Figure 2e. Strong, Mid, and Weak refer to transcriptional promoters and RBS sequences as indicated Table S4. Promoter 1 and RBS 1 control the expression of RFP and Spinach, while the repressor EsaR is under the control of Promoter 2 and RBS 2. Sequences were generated from FC

22 Figure S19. A gradient picture is generated by applying different genetic parts to the repressor circuit. Seven of the tested repressor circuits were arranged from the weakest to the strongest in terms of final concentration of the reporter protein, RFP. Each pixel, therefore, contained a single DNA template with the repressor circuit employing different genetic parts to tune both transcription and translation of the reporter gene. On the left the predicted pictures are shown as determined by a computational model (Figure S10) that took into consideration the noise associated with both transcription and translation. For a list of the constructs used for each pixel see Table S17. Data are the fluorescence after 6 h of expression with the PURE system at 37 C in a 384-well plate. Figure S20. The concentration of template DNA does not significantly impact the ratio of protein expression in on and off states. Reactions were either in the presence (ON) or absence (OFF) of 5 μm 3-oxohexanoyl-homoserine lactone. The values on the x-axis represent the concentration of DNA template. The genetic construct used had a strong promoter and a strong RBS to control the expression of RFP and Spinach, while the repressor was under the control of middle intensity promoter and a middle intensity RBS. RNA was quantified by the fluorescence of the Spinach aptamer and protein by the fluorescence of RFP. Data are the fluorescence after 6 h of expression with the PURE system at 37 C. 22

23 Figure S21. Modifying the GC content of the coding sequence leads to differences in translation but not transcription. Variants of the blue fluorescent protein Azurite were generated with different percentages of GC content in order to probe the relationship between mrna structure and protein expression in PURE system reactions. Different PURE system reactions were incubated for 6 hours at 37 C with the different coding sequences and both transcription and translation was monitored using fluorescence. Samples tested were, from lower GC content to higher GC content the following: NG046 (AT rich) (GC content: 30.4%), NG045 (original sequence) (GC content: 36.27%), NG047 (codon optimized sequence) (GC content: 47.14%), NG048 (GC rich) (GC content: 59.97%). Supporting References (1) Lentini, R., Forlin, M., Martini, L., Del Bianco, C., Spencer, A. C., Torino, D., and Mansy, S. S. (2013) Fluorescent Proteins and in Vitro Genetic Organization for Cell-Free Synthetic Biology. ACS Synth. Biol. 2, (2) Chizzolini, F., Forlin, M., Cecchi, D., and Mansy, S. S. (2014) Gene position more strongly influences cell-free protein expression from operons than T7 transcriptional promoter strength. ACS Synth. Biol. 3, (3) Salis, H. M. (2011) Chapter two The Ribosome Binding Site Calculator, in Methods in Enzymology, pp