Mutalik QuantEstimation Supplement revised - Nature · p2 p5 p3 p4 p7 p6 p1 u4 u1 u7 u6 u5 u3 u2 u8...

1

Supplemental Information

Quantitative Estimation of Activity and Quality for Collections of Functional Genetic Elements

Vivek K. Mutalik1,2,3,9, Joao C. Guimaraes1,3,4,9, Guillaume Cambray1,3,9, Quynh-Anh Mai1,3,

Marc Juul Christoffersen1,3, Lance Martin1,3,8, Ayumi Yu1,3,8, Colin Lam1,3, Cesar Rodriguez1,3,8,

Gaymon Bennett1,3,8, Jay D. Keasling1,2,3,6,7, Drew Endy1,5,9,*, Adam P. Arkin1,2,3,9,*

1 BIOFAB International Open Facility Advancing Biotechnology (BIOFAB), 5885 Hollis Street,

Emeryville, CA 94608, USA

2 Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720,

USA

3 Department of Bioengineering, University of California, Berkeley, CA, 94720, USA

4 Department of Informatics, Computer Science and Technology Center, University of Minho,

Campus de Gualtar, Braga, Portugal

5 Department of Bioengineering, Stanford University, Stanford, CA 94305, USA

6 Department of Chemical & Biomolecular Engineering, University of California, Berkeley, CA,

94720, USA

7Joint Bioenergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA

8 Present Addresses: Dept. of Bioengineering, Stanford University, Stanford, CA 94305,

USA (L. M.); Philotic, Inc. 88 Kearny St, Suite 2100, San Francisco, CA 94108, USA

(A. Y.); Autodesk, Inc. One Market Street, Suite 200, San Francisco, CA 94105 (C. R.);

Center for Biological Futures, Fred Hutchinson Cancer Research Center, 1100 Fairview

Ave. Seattle, WA 98109 (G. B.).

9 Equal contribution

*Correspondence should be addressed to D.E. or A.P.A. ([email protected]; [email protected])

Nature Methods: doi:10.1038/nmeth.2403

2

Table of Contents

Supplementary Figures

SF1. Combinatorial assembly of Promoters and 5’ UTRs 3

SF2. Quality and reproducibility of characterization pipeline 4

SF3. Comparison of plasmid-born vs chromosomally integrated

Promoter:5’ UTR combinatorial library driving gfp expression 5

SF4. Observed variation and correlation of mRNA abundance and

fluorescence from combinatorial library of expression elements 6

SF5. Estimation of expression element performance scores using transcript abundance

and translation efficiency datasets 7

SF6. Estimation of part activity with limited measurements 8

SF7. Genetic element performance scores at two different temperatures 9

SF8. Variability in 5’ UTR scores is correlated with RNA folded structure at the

UTR:GOI junction. 10

Supplementary Tables

ST1. Putative transcription and translational elements used in this work 11

ST2. ANOVA table for main expression elements and their interaction 13

ST3. Process Steps and Costs for BIOFAB Quant. Estimation Pilot Study 14

ST4. List of plasmids and strains used in this work 15

ST5. List of primers used in the present work 18

Supplementary Note 22

References 25


3

Supplementary Figure 1: Combinatorial assembly of promoters and 5’ UTRs (A) The

vector backbone of pFABOUT2 (gfp) was PCR amplified using primers oFAB57 and oFAB58,

which introduces BsaI target sites (shown as green and purple boxes with arrow head) by

replacing the TetR coding region, Ptet and 5’ UTR driving the expression of gfp gene. The

terminators (symbol ‘T’) and promoters (line-arrow) on the vector backbone as shown in the

figure. The purified PCR products were then digested with enzyme BsaI and yield

pFABOUT2_cut with overhangs as shown in the figure. The cut vector backbone was then

ligated to phosphorylated-annealed oligos encoding the promoter and 5’ UTR to yield 84 (seven

promoters and eleven 5’ UTRs, and one Null-RBS 5’ UTR as a control) constructs that make up

the GFP library. (B) The vector backbone of pFABOUT18 (rfp) was PCR amplified using

primers oFAB58 and oFAB60 to introduce BsaI sites (shown as red and purple boxes with arrow

head) upstream of reporter. The cut vector pFABOUT18_cut was then ligated to phosphorylated-

annealed oligos encoding promoters and 5’ UTR to yield 84 constructs of RFP library. Note that

both GFP and RFP libraries have a common four nucleotide TTTG junction between promoters

and 5’ UTRs.


4

Supplementary Figure 2: Quality and reproducibility of measurements (a) Comparison

between plate reader and flow-cytometer measurements across 77 combinations of 7 Promoters

and 11 UTRs driving gfp expression (error bars indicate standard deviation across three

biological replicates). The regression correlation (excluding one outlier in grey, at very low

expression level) is 0.981, and R2=0.962. (b) Comparison between plate reader and flow-

cytometer measurements across 77 combinations of 7 Promoters and 11 UTRs driving rfp

expression (error bars indicate standard deviation across three biological replicates). The

regression correlation (excluding two outliers in grey, at very low expression level) is 0.980 and

R2=0.961. (c) Comparison of two biological replicates of flow cytometer measurements. The

correlation between (log) intensities of replicate measurements lies between 0.984 and 0.995. An

example is shown in the figure (Replicate 1 vs Replicate 2), with r=0.986 and R2=0.972

(N=154). The correlation for the measurements using plate reader lies between 0.995 and 0.998.

(d) Transcript abundance was measured in three replicate biological samples. The correlation

between (log) intensities of replicate measurements lies between 0.788 and 0.907. A

representative example is shown in the figure (Replicate 1 vs Replicate 2), with r=0.907 and

R2=0.823 (N=154).

a b

c

18 20 22 24 26

18

20

22

24

26

Replicate 1 - mRNA abundance (Molecules/μl, log2)

Re

plic

ate

2 -

mR

NA

ab

un

da

nce

(M

ole

cu

les/μ

l, lo

g2

)

R2= 0.823

2 4 6 8 10

24

68

10

Replicate 1 - Expression strength by Flow Cytometry (A.U., log2)

Re

plic

ate

2 -

Exp

ressio

n s

tre

ng

th b

y F

low

Cyto

me

try (

A.U

., lo

g2

)

R2= 0.972

d

12 14 16 18 20 22

24

68

GFP expression strength by Plate Reader (RNU/OD/h, log2)

GF

P e

xp

ressio

n s

tre

ng

th b

y F

low

Cyto

me

try (

A.U

., lo

g2

)

R2= 0.962

8 10 12 14 16 18 20

34

56

78

9

RFP expression strength by Plate Reader (RNU/OD/h, log2)

RF

P e

xp

ressio

n s

tre

ng

th b

y F

low

Cyto

me

try (

A.U

., lo

g2

)

R2= 0.961


5

Supplementary Figure 3. Comparison of plasmid-born vs chromosomally integrated

Promoter:5’ UTR combinatorial library driving gfp expression. Scatter plots of fluorescence

(GFP) measurements from 63 pairs of plasmid-born versus chromosomally integrated Promoter-

5’ UTR combination. A linear regression on 60 points gives the following relationship: Fplasmid =

12.07 * Fchromosomal – 18.03 (R2 of 0.85). These data fit with the expected dosage difference

between a p15A-born and chromosomally integrated gene (~12-15 versus ~1 copy).

10 20 30 40 50 60

0100

200

300

400

500

600

Mean fluorescence on chromosome (A.U.)

Mean flu

ore

scence o

n p

lasm

id (

A.U

.) y = 12.07 * x - 18.03R2 = 0.85


6

Supplementary Figure 4: Observed variation and correlation of mRNA abundance and

fluorescence from combinatorial library of expression elements. Scatter plot of mRNA

abundance versus fluorescence for constructs driving gfp (a) and rfp (b) expression. Pair-wise

comparison between mRNA levels (c) and fluorescence (d) for GFP and RFP library. Data

points are coded with a different symbol for every pi and different colors for ui, according to the

legend.

a b

GF

P F

luo

resc

en

ce (

A.U

., lo

g2

)

GFP mRNA abundance (Molecules/µL, log2) RFP mRNA abundance (Molecules/µL, log2)

RF

P F

luo

resc

en

ce (

A.U

., lo

g2

)

RFP Fluorescence (A.U., log2)

GF

P F

luo

resc

en

ce (

A.U

., lo

g2

)

3 4 5 6 7 8 9

2

4

6

8

R2=0.38

18 19 20 21 22 23

2

4

6

8

R2=0.58

20 21 22 23 24 25 26

3

4

5

6

7

8

9

R2=0.59

p1

p2

p3

p4

p5

p6

p7

u1

u2

u3

u4

u5

u6

u7

u8

u9

u10

u11

dc

20 21 22 23 24 25 26

18

19

20

21

22

23

RFP mRNA abundance (Molecules/µL, log2)

GF

P m

RN

A a

bu

nd

an

ce (

Mo

lecu

les/

µL

, lo

g2

)

R2=0.38


7

Supplementary Figure 5: Estimation of expression element performance scores using

transcript abundance and translation efficiency datasets. (a) Performance score based on

transcript abundance: An element score (height of the bars) provides an estimate of the average

contribution of each transcriptional element Pi, translational element Uj and GOIk to measured

mRNA abundance. The error bars show the standard error of this score resulting from changes in

proximal genetic context and reflect the reliability of the element. These variations result from

interaction between different: transcriptional elements and GOIs (black), transcriptional and

translational elements (blue), and translational elements and GOIs (green). (b) Performance

score based on translational efficiency: An element score (height of the bars) provides an

estimate of the average contribution of each transcriptional element Pi, translational element Uj

and GOIk to translation efficiency (fluorescence per mRNA). The error bars show the standard

error of this score resulting from changes in proximal genetic context and reflect the reliability of

the element. These variations result from interaction between different: transcriptional elements

and GOIs (black), transcriptional and translational elements (blue), and translational elements

and GOIs (green).

a

b

p2

p5

p3

p4

p7

p6

p1

u4

u1

u7

u6

u5

u3

u2

u8

u9

u11

u10

rfp

gfp

Part

score

-7-6

-5-4

-3-2

-10

p4

p1

p5

p2

p3

p6

p7

u7

u4

u10

u11

u1

u6

u3

u8

u5

u9

u2

gfp rfp

Part

score

01

23

45

67

89

Promoter:GOI interaction e�ects

Promoter:5’ UTR interaction e�ects

5’ UTR:GOI interaction e�ects


8

Supplementary Figure 6: Estimation of part activity with limited measurements. (a)

Estimated activities of all promoters with increasing numbers of 5’ UTRs. (b) Estimated

activities of all 5' UTRs with increasing number of promoters. Each dot represents a unique

combination of elements and the single part with best accuracy is colored in red, whereas the pair

of parts with higher accuracy is colored in blue.

a-2

02

46

Number of UTRs used

Estim

ate

d s

core

for

p1

1 2 3 4 5 6 7 8 9 10 11

-20

24

6

Number of UTRs used

Estim

ate

d s

core

for

p2

1 2 3 4 5 6 7 8 9 10 11

-20

24

6

Number of UTRs used

Estim

ate

d s

core

for

p3

1 2 3 4 5 6 7 8 9 10 11

-20

24

6

Number of UTRs used

Estim

ate

d s

core

for

p4

1 2 3 4 5 6 7 8 9 10 11

-20

24

6

Number of UTRs used

Estim

ate

d s

core

for

p5

1 2 3 4 5 6 7 8 9 10 11

-20

24

6

Number of UTRs used

Estim

ate

d s

core

for

p6

1 2 3 4 5 6 7 8 9 10 11

-20

24

6

Number of UTRs used

Estim

ate

d s

core

for

p7

1 2 3 4 5 6 7 8 9 10 11

b

-20

24

6

Number of Promoters used

Estim

ate

d s

core

for

u4

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u3

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u2

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u1

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u8

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u7

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u6

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u5

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u11

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u10

1 2 3 4 5 6 7

-20

24

6


Estim

ate

d s

core

for

u9

1 2 3 4 5 6 7


9

Supplementary Figure 7: Genetic elements performance across two temperatures. To assess the

effect of different temperatures on transcription and translational elements we grew and measured the

Promoter:5’ UTR combinatorial library driving a GFP reporter at 30C. We next employed ANOVA

analysis (as detailed in online Methods) to estimate the effect of temperature on overall gene expression

as well as its part-specific effect. (a) Fluorescence measured at two temperatures. Gene expression

measured at 30C and 37C are highly correlated. (b) ANOVA analysis indicates that temperature effect on

overall expression is very weak (0.01%). We also observed that temperature has a greater impact on

Promoters (0.21%) than on 5’ UTRs (0.12%), though both effects are also very weak. (c) Estimation of

parts scores and deviations across different temperature conditions (30C and 37C) indicate that the

promoter and 5’ UTR elements tested here appear insensitive to the given change in temperature (red and

green error bar); both p5 and u4 parts show higher variability in their performance at two different

temperature measured.

a b

c

Experimental Error (0.02%)

Promoter:UTR:Temperature (0.07%)

Promoter:Temperature (0.21%)

UTR:Temperature (0.12%)

Promoter:UTR (2.14%)

UTR (69.9%)

Promoter (27.53%)

Temperature (0.01%)

Promoter:Temperature interaction e�ects

Promoter:5’UTR interaction e�ects

5’UTR:Temperature interaction e�ects

2 4 6 8 10

02

46

81

0

GFP expression at 37C (A.U., log2)

GF

P e

xpre

ssio

n a

t 3

0C

(A

.U.,

log

2) R

2= 0.974

p5

p2

p3

p4

p1

p6

p7

u7

u1

u4

u1

1

u1

0

u6

u8

u9

u5

u3

u2

30

37

Pa

rt s

core

01

23

45

6


10

Supplementary Figure 8: Variability in 5’ UTR scores is correlated with RNA folded structure at

the UTR:GOI junction. To evaluate the source of variability of 5’ UTR scores we used UNAfold

software1 to predict RNA structure formed at the junction between all the 11 5’UTRs and 2 GOI ([-19,31]

with respect to ATG). We observed that different RNA structures are formed when the same UTR is used

with different GOIs. (a) For a 5’ UTR, the weaker the structure formed the higher is expected to be its

activity. For example, if the structure formed with GFP is weaker than the one formed with RFP (space

above dashed line), the activity of the 5’ UTR fused with GFP is expected to be higher than when fused

with RFP and vice-versa. 5’ UTRs driving stronger GFP expression than RFP are colored in orange,

while those driving stronger RFP expression than GFP are colored in blue. Using this qualitative metric

we find that 9 out of the 11 UTRs in our dataset have the expected behavior. (b) The difference between

the folding energies of GFP and RFP (∆∆G) are well correlated (r=0.77) with the difference in UTR

performance between GFP and RFP (∆Score). When ∆Score > 0 it means that UTR performance is higher

with GFP than RFP (orange dots), conversely if ∆Score < 0 it means that UTR performance is higher with

RFP than GFP (blue dots). UTRs that have stronger structures when fused with GFP than with RFP have

∆∆G < 0 and UTRs that have stronger structures with RFP than GFP have ∆∆G > 0.

a

-10 -8 -6 -4

-10

-8-6

-4

MFE for RFP

MF

E for

GF

P

Ui(GFP) > Ui(RFP)

Ui(RFP) > Ui(GFP)

a a g a g g g

g

a

c

a

a

a

c

t

a

g

t

a

t

g

a

g

c

a

a

a

g

g

a

g

a a

g

a

a

c

t

t

t

t

c

a

c

t

g

g

a

g

t

t

g

t

c

c

1

10

20

30

40

50

55

a a g a g g

g

g

a

c a

a

a

c

t a g

t

a

t

g

g

c

g

a

g

t

a

g

c

g

a

a g

a

c

g

t

t

a

tc

a

a

a

g

ag

t

t

c

a

t

g

c

1

10

20

30

40

50

55

a a a g a g g

a

g

a a

a

t

t a a g

c

a

t g

a

g

c a a a g g

a

g

a

a

g

a a

c

t

t

t

t

c a c t g

g

a

g

t t

g

t

c

c

1

10 20

30

40

50

55

a a a g a g g

a

g

a a

a

t

t a a g

c

a

t

g

g

c

g

a

g

t

a

g

c

g

a

a g

a

c

g

t

t

a

tc

a

a

a

g

ag

t

t

c

a

t

g

c

1

1020

30

40

50

55

U1:GFP U1:RFP

U5:GFP U5:RFP

b

-6 -4 -2 0

-2-1

01

2

2

ΔΔG

ΔS

core

Ui(GFP)

higher

score

Ui(RFP)

higher

score

Ui:GFP

stronger

structure

Ui:RFP

stronger

structure

ΔG=-6.8 ΔG=-8.2

ΔG=-11.6 ΔG=-5.5


11

Supplementary Tables: Supplementary Table 1

A: Putative transcriptional and translational elements used in this work. List of promoter

and 5’ UTR sequences used in the present work with their corresponding lengths. Promoters are

highlighted with their transcription start sites (+1, red), promoter -35 and -10 motifs are indicated

in red and purple respectively, and an extended -10 motif is colored yellow. For 5’ UTRs,

putative Shine-Dalgarno sequences are highlighted in red.

aThe promoter names used in this work and their sources:

p1:J23101(http://partsregistry.org/Part:BBa_J23101), p2: pLTeto1 (ref 2), p3: galP1 (ref 3),

p4:placUV5 (ref 3), p5:pLlaco1 (ref 2), p6:Ptrc3, p7:Ptet4;

bThe 5’ UTRs and their sources:

u1:Anderson (http://partsregistry.org/wiki/index.php/Part:BBa_J61100), u2:Bujard2, u3:B0030

(http://partsregistry.org/Part:BBa_B0030), u4:B0032 (http://partsregistry.org/Part:BBa_B0032),

u5:B0034 (http://partsregistry.org/Part:BBa_B0034), u6:GSG5, u7:GSGV6, u8:Invitrogen®,

u9:JBEI4, u10:Plotkin7, u11:Alon8 , u12:DeadRBS (this work).

B: Total length of 5’ UTR range from 21 to 59 nucleotides. Note that both GFP and RFP

libraries have a common four nucleotide TTTG junction between promoters and 5’ UTRs

(Supplementary Fig. 1). The order of the elements in the matrices corresponds to a bidimensional

clustering performed on the data in panels a-d of Fig. 2.


12

A

ID Promotersa Sequence length

p1 PJ23101 TTTACAGCTAGCTCAGTCCTAGGTATTATGCTAGC -35+2

p2 PLteto1

TCCCTATCAGTGATAGAGATTGACATCCCTATCAGTGATAGAGATACTGAGCACATCAGCAGGACGCACTGACC -54+19

p3 galP1 ATTCCACTAATTTATTCCATGTCACACTTTTCGCATCTTTGTTATGCTATGGTTATTTCATACCATAA -59+8

p4 PlacUV5 CCCCAGGCTTTACACTTTATGCTTCCGGCTCGTATAATGTGTGGAATTGTGAG -43+8

p5 PLlacO1

ATAAATGTGAGCGGATAACAATTGACATTGTGAGCGGATAACAAGATACTGAGCACATCAGCAGGACGCACTGACC -56+19

p6 Ptrc TTGACAATTAATCATCCGGCTCGTATAATGTGTGGAATTGTGAG -35+8

p7 Ptet

TAATTCCTAATTTTTGTTGACACTCTATCGTTGATAGAGTTATTTTACCACTCCCTATCAGTGATAGAGAAAA -51+20

ID 5’ UTRsb Sequence length

u1 Anderson TCTAGAGAAAGAGGGGACAAACTAGT 26

u2 Bujard GAATTCATTAAAGAGGAGAAAGGTACC 27

u3 B0030 ATTAAAGAGGAGAAATTAAGC 21

u4 B0032 TCACACAGGAAAGGCCTCG 19

u5 B0034 AAAGAGGAGAAATTAAGC 18

u6 GSG TAAGGAGGTGACAAT 15

u7 GSGV GCTCTTTAACAATTTATCATAAGGAGGTGACAAT 34

u8 Invitrogen AAAATTAAGAGGTATATATTA 21

u9 JBEI GAATTCAAAAGATCTTTTAAGAAGGAGATATACAT 35

u10 Plotkin TGGATCCAAGAAGGAGATATAACC 24

u11 Alon GGATCCTCTAGATTTAAGAAGGAGATATACAT 32

u12

DeadRBS control

(Null-RBS) CACCATACACTG 12

B

u7 u1 u4 u11 u10 u6 u8 u9 u5 u3 u2

p5 57 49 42 55 47 38 44 58 41 44 50

p2 57 49 42 55 47 38 44 58 41 44 50

p4 46 38 31 44 36 27 33 47 30 33 39

p3 46 38 31 44 36 27 33 47 30 33 39

p1 40 32 25 38 30 21 27 41 24 27 33

p6 46 38 31 44 36 27 33 47 30 33 39

p7 58 50 43 56 48 39 45 59 42 45 51


13

Supplementary Table 2: ANOVA table for main expression elements and their interactions Sum of squares represent the actual explanation of variation in the output measurement (fluorescence, transcript abundance and translational efficiencies). The mean squares represent the average contribution of each of the factors/interactions taking into account their degrees of freedom (df).(a) ANOVA table for fluorescence dataset. (b) ANOVA table for mRNA dataset. (c) ANOVA table for translational efficiency dataset. a: ANOVA table for Fluorescence

df Sum

squares

%

Mean

squares

%

Mean sq. after removing GOI

% F value P value

Promoter 6 421.3 22.1 70.2 36.0 70.2 37.0 1356.6 3.7E-218 UTR 10 865.7 45.5 86.6 44.4 86.6 45.6 1672.5 9.6E-262 GOI 1 5.0 0.3 5.0 2.6 0.0 0.0 96.2 6.1E-20

Promoter:UTR 60 227.7 12.0 3.8 1.9 3.8 2.0 73.3 2.7E-150 Promoter:GOI 6 13.1 0.7 2.2 1.1 2.2 1.2 42.0 2.5E-37

UTR:GOI 10 255.5 13.4 25.6 13.1 25.6 13.5 493.6 5.1E-183 Promoter:UTR:GOI 60 98.7 5.2 1.6 0.8 1.6 0.9 31.8 6.8E-101

Residuals 308 15.9 0.8 0.1 0.0 0.1 0.0 b: ANOVA table for mRNA abundance

df Sum

squares %

Mean squares

%

Mean sq. after

removing GOI

% F value P value




Residuals 308 191.0 11.5 0.6 0.1 0.6 0.8 NA NA c: ANOVA table for translation efficiency

df Sum

squares

% Mean square

s %

Mean sq. after

removing GOI

% F value P value




Residuals 308 223.6 12.6 0.7 0.1 0.7 0.8 NA NA


14

Supplementary Table 3: Process Steps and Costs for BIOFAB Quant. Estimation Pilot Study

(168 plasmid-based constructs plus 57 chromosomal integrations)

Task Staffing Time (hours)

Materials & Supplies Costs (USD)

Design (part selection & sequences, junction architecture, cloning strategy)

1 senior researcher

80 n/a $5,000

Build (oligo. synthesis, assembly, sequence verification)

1 senior researcher, 2 research assistants

360 (120 per) Oligonucleotides ($550), enzymes ($1144), sequencing, ($1650), tips, media, plates ($500). $3,844 total or $17.08 per vector.

$19,500 $3,844

Measurements (plate reader, cytometry, qPCR)

1 senior researcher, 2 research assistants

360 (120 per) Triplicate assays for 225 constructs by plate reader and cytometry. Additional triplicate assays for 168 plasmid-based constructs by qPCR. Cost per plate reader or cytometer assay ($3.00/construct). Cost per qPCR assay $20.00/construct). $12,000 total.

$19,500 $12,000

Analysis (data processing, numerical methods)

1 senior researcher, 1 graduate student (not charged)

120 (60 per) n/a $3,750

Totals n/a 920 hours n/a $63,594

All costs calculated for low-to-medium throughput assays carried out in a rent free BSL1 facility equipped with a -80C freezer, -20C freezer, deli fridge, 1 microplate shaker, multichannel pipettes, a microplate reader, a flow cytometer, and a qPCR machine. Salaries include overhead and benefits and are approximate to within 10%.


15

Supplementary Table 4: List of plasmids and strains used in the present work

Column A: Promoter name (generic or source name)

Column B: Promoter name used in the main text

Column C: Abstract part number for promoter element indicated as apFAB #

Column D: 5’UTR name (generic or source name)

Column E: 5’UTR name used in the main text

Column F: Abstract part number for 5’UTR element indicated as apFAB #

Column G: Plasmid number for the combinatorial library with GFP reporter

Column H: Strain number with GFP library

Column I: Plasmid number for the combinatorial library with RFP reporter

Column J: Strain number with RFP library

Column K: Strain number for the combinatorial library with GFP reporter on the chromosome.


16

A B C D E F G H I J K


17


18

Supplementary Table 5: List of primers used in the present work

Column A: Oligonucleotide numbers: oFAB #. Primers used for sequencing are denoted as soFAB #.;

Column B: Forward and reverse primers are indicated as FW and RV; Column C: Information notes for

the primer; Column D: Primer Sequences (5’ to 3’)

A B C D


19


20


21


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Supplementary Note

Quantitative estimate of the time, effort, costs that are required to perform genetic part

characterization in BioFAB like facility

Ideally, when introducing a new biological part into a cell one could predict its operation

from a first principles model. However, the first principles necessary to understand part

function within a cellular milieu are often not clear. Uncertainty in the proper model can

be placed in at least three classes: (i) the mechanism underlying the model, (ii) the values

of kinetic and thermodynamic parameters of that model in vivo, and, most difficult, (iii)

uncertainty regarding what other processes may impact the part operation. In this latter

class, for cellular systems, there are likely a bevy of direct interactions and interferences

with currently uncharacterized or unknown cellular processes not included in the model,

and then indirect effects on cellular resources affecting fitness and stability of the host.

It is inevitable, therefore, that though the known physics will inform and constrain the

model for the part and its interactions with other cellular factors, there will likely have to

be some leeway for modeling interactions for which there is no known mechanism. A

low order regression model is a good approximation for most cases and this is what we

have presented here for gene expression parts. Such an approach is also what has been

used in representing classical sequence/structure/activity relationships with proven

industrial utility9, 10. Such models are most useful when the part classes are limited and

the number of factors being considered is relatively small (i.e., a main effect plus a small

number of interactions). Different classes of elements are likely to have different modes

of interaction with other elements and thus make the model more complex to capture all

possibilities. Stated differently, it is easier to parameterize such models when the part

classes used to vary a functional feature, say translation initiation, have like mechanisms

such that similar underlying models can be used to describe part performance. Once a

class of models exist for a given part family along with an understanding of possible

context mediators (i.e., interactions amongst parts, the cellular context, and external

environment) and the activity variables one wishes to track, it is possible to calculate how

the process of characterizing parts scales (to some degree).

In the example developed here, we use mRNA levels and total protein fluorescence as

activity variables and two classes (parts families) of gene expression controllers (i.e.,

“promoter” and “5’ UTR”) that we assert affect mostly transcription and translation

initiation, respectively, with some additional effect on mRNA stability by both part

classes. There are also two context mediators: interaction amongst parts and the gene they

are driving, and the temperature change tested here. We use a factorial ANOVA to

analyze how all variables impact mRNA and protein values to derive the estimated


23

activities of parts (ANOVA main effects) and interactions. The form of the model and the

goal of using such a model to estimate part scores leads to questions re: how many

constructs must be measured to achieve a particular confidence in estimated scores.

Ideally, we would hope to have sets of parts that collectively represent of range of

activities. If we assume that the constructs to be assayed are drawn from a random

combinatorial library composed from such sets then we could ask how many of these we

would have to screen to derive canonical scores for the parts their interactions. For

factorial ANOVA-based models we can use standard formulas for a priori statistical

power to derive the number of constructs necessary to classify these scores into some

number of levels (Reference: Sample Size Calculations: Practical Methods for Engineers

and Scientists, Paul Matthews ISBN:0615324614). To use these formulas we need to

specify a desired maximum false positive and false negative rate and an expected effect

size. The effect size can be estimated from prior data or standard rules-of-thumb. For

example, we might expect to distinguish different “strengths” for each of the promoters

and UTRs assuming about five different distinguishable strengths for each and moderate

interactions among all elements. We then further assert two “levels” for the genes (RFP

and GFP). Thus, there are 5*5*2 groups. To reduce the probability of falsely identifying

two elements as identically strong to below 20% and a significance level of 0.05, we

would need approximately 200 randomly chosen individual constructs made from

representative promoters and UTRs. As the number of variables and levels increase, or

the effect sizes become smaller, or the required false positive and false negative rates

drop, the number of samples goes up rather rapidly and costs go up.

In this manuscript, the parts families are small and perhaps not entirely representative,

and we analyzed an exhaustive combinatorial library. While this approach ensures we

have scores for every element with the maximum statistical power possible given the size

of the families we started with, ultimately for these models to be the most useful we

would need three things.

First, it would be ideal for the parts families to be composed of members that were

mechanistically homogeneous (as noted above, so that the same model faithfully applies

to each element), represented a wide range of activities (as done here), and with as much

sequence variation as possible (to exercise all the possible idiosyncratic interactions with

other parts and context variables, akin as done here).

Second, these parts families should be engineered to have as well-insulated function as

possible (not done in this manuscript, but addressed in the accompanying manuscript11).

In the present case, for example, the promoter library had differing lengths of 5’ UTR at

one end thereby adding elements to the transcript that could affect mRNA stability and


24

translation initiation. Similarly, there was a strong interaction between the 5’ UTR and

the downstream gene. Thus multiple features impacting mRNA and fluorescent protein

levels are changed by each member of the library leading to more complex interactions

and the need for a full factorial model. In Lei et al.12, for example, it is shown how

standardized transcript cleavage can insulate promoter and 5’UTR function and in the

companion paper to this manuscript11 we show how to insulate 5’UTR and gene function

thus simplifying the model, thereby theoretically reducing the amount of characterization

necessary.

Third, parts would be characterized in standard ways over the range of compositions and

contexts most useful for any particular application (in this manuscript we are focused on

supporting laboratory research as the application). Once an initial model is well

characterized, any new member of a part family can be effectively characterized with

many fewer samples than needed to create the initial part characterizations with some

cost in error (Figure 5, main text). Such efforts can be distributed outside a single facility

as long as individuals are following standard characterization protocols that don’t

exercise variables not captured in the central model, or if they do exercise such variables

they are captured effectively as metadata.

The entire cost for the process then is the cost to make and characterize the N samples

needed to realize the initial models (Supplementary Table 3). That is, the total cost is

N*((cost to make a construct)+(cost to assay)+(cost to process data)) + (cost to calculate

parameterized model). The latter term is usually negligible. Note that this representation

does not account for the cost to design standard biological parts that operate robustly and

homogeneously across changing contexts (see companion paper).

In summary, for ubiquitous functions like gene expression controllers, core metabolic

activities, and perhaps elements that target macromolecules to different locations or

processing machinery, it will be fruitful for BIOFABs to take on the bulk of the part

choice, composition design, and subsequent characterization. Individual variants not

present in BIOFAB libraries can then be characterized by individual users and made

useful to others by donating information to public domain repositories that use such data

to improve models. Specialty parts that are used in only a few applications will ultimately

fall into the domain of the particular stakeholder for those applications.


25

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