Fine-tuning gene networks using simple sequence repeats.
ABSTRACT The parameters in a complex synthetic gene network must be extensively tuned before the network functions as designed. Here, we introduce a simple and general approach to rapidly tune gene networks in Escherichia coli using hypermutable simple sequence repeats embedded in the spacer region of the ribosome binding site. By varying repeat length, we generated expression libraries that incrementally and predictably sample gene expression levels over a 1,000-fold range. We demonstrate the utility of the approach by creating a bistable switch library that programmatically samples the expression space to balance the two states of the switch, and we illustrate the need for tuning by showing that the switch's behavior is sensitive to host context. Further, we show that mutation rates of the repeats are controllable in vivo for stability or for targeted mutagenesis-suggesting a new approach to optimizing gene networks via directed evolution. This tuning methodology should accelerate the process of engineering functionally complex gene networks.
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ABSTRACT: The next generation of synthetic biology applications will increasingly involve engineered organisms that exist in intimate contact with humans, animals and the rest of the environment. Examples include cellular and viral approaches for maintaining and improving health in humans and animals. The need for reliable and specific function in these environments may require more complex system designs than previously. In these cases the uncertainties in the behavior of biological building blocks, their hosts and their environments present a challenge for design of predictable and safe systems. Here, we review systematic methods for the effective characterization of these uncertainties that are lowering the barriers to predictive design of reliable complex biological systems.Current opinion in chemical biology 11/2013; · 8.30 Impact Factor
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ABSTRACT: Global energy demand and environmental concerns have stimulated increasing efforts to produce carbon-neutral fuels directly from renewable resources. Microbially derived aliphatic hydrocarbons, the petroleum-replica fuels, have emerged as promising alternatives to meet this goal. However, engineering metabolic pathways with high productivity and yield requires dynamic redistribution of cellular resources and optimal control of pathway expression. Here we report a genetically encoded metabolic switch that enables dynamic regulation of fatty acids (FA) biosynthesis in Escherichia coli. The engineered strains were able to dynamically compensate the critical enzymes involved in the supply and consumption of malonyl-CoA and efficiently redirect carbon flux toward FA biosynthesis. Implementation of this metabolic control resulted in an oscillatory malonyl-CoA pattern and a balanced metabolism between cell growth and product formation, yielding 15.7- and 2.1-fold improvement in FA titer compared with the wild-type strain and the strain carrying the uncontrolled metabolic pathway. This study provides a new paradigm in metabolic engineering to control and optimize metabolic pathways facilitating the high-yield production of other malonyl-CoA-derived compounds.Proceedings of the National Academy of Sciences of the United States of America. 07/2014;
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ABSTRACT: Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.PLoS Genetics 06/2014; 10(6):e1004407. · 8.52 Impact Factor
Fine-tuning gene networks using simple
Robert G. Egbert and Eric Klavins1
Department of Electrical Engineering, University of Washington, Seattle, WA 98195
Edited by Charles R. Cantor, Sequenom, Inc., San Diego, CA, and approved August 2, 2012 (received for review April 23, 2012)
The parameters in a complex synthetic gene network must be ex-
tensively tuned before the network functions as designed. Here,
we introduce a simple and general approach to rapidly tune gene
networks in Escherichia coli using hypermutable simple sequence
repeats embedded in the spacer region of the ribosome binding
site. By varying repeat length, we generated expression libraries
that incrementally and predictably sample gene expression levels
over a 1,000-fold range. We demonstrate the utility of the ap-
proach by creating a bistable switch library that programmatically
samples the expression space to balance the two states of the
switch, and we illustrate the need for tuning by showing that
the switch’s behavior is sensitive to host context. Further, we show
that mutation rates of the repeats are controllable in vivo for sta-
bility or for targeted mutagenesis—suggesting a new approach
to optimizing gene networks via directed evolution. This tuning
methodology should accelerate the process of engineering func-
tionally complex gene networks.
gene network optimization ∣ evolvability ∣ synthetic biology
moters, ribosome binding sites, and protein coding regions often
behave unexpectedly when used in novel designs. Noise (1, 2),
metabolic load (3), poorly characterized interactions with the
host (4), and general uncertainty about the detailed functionality
of parts conspire to limit the complexity of synthetic gene net-
works to a small number of interacting genes (5, 6). As a result,
a complex gene network that has been predicted analytically to
perform well may in fact perform poorly, if it works at all, when
ultimately implemented in cells. Furthermore, even if a gene net-
work can be made to perform well in a particular strain and en-
vironment, there is no guarantee that the same network will
perform well if ported to a new strain or environment (7). More
generally, synthetic networks often operate in substantially differ-
ent parameter regimes than expected during the design process
and must, therefore, be tuned (2, 8–11) before they function
properly, and even retuned when used in new environments or
with different hosts.
One way to improve the performance of a poorly tuned gene
network is to introduce focused variability into the design, gen-
erating a library of circuits (12–15) with the same genetic com-
ponents and connectivity, but with each member of the library
operating in a different parameter regime. In bacteria, for exam-
ple, promoters (16, 17), ribosome binding sites (RBS) (18, 19),
RNA stability (20, 21), protein stability (22), and other biochem-
ical details such as transcription factor regulation (23) or enzyme
catalysis (24) can be varied to sample different regions of para-
meter space. Furthermore, sensitivity analysis (25) can guide the
designer to parameters that, when tuned, are most likely to result
in improved performance. Subject to screening or selection, the
effectiveness of a tuning library is proportional to the range of
parameters it samples and is inversely proportional to its size.
More specifically, a useful tuning approach explores the para-
meter space over a large range with high resolution; results in
a predictable relationship between the genetic sequence and
the values of the corresponding parameters; and is scalable to
ngineering reliable and predictable synthetic gene networks
presents unique challenges because genetic parts such as pro-
complex networks. Ideally, a good tuning method should also
be evolvable, forcing the host organism to focus mutations on
highly tunable elements in the network, such that it complements
directed evolution techniques (26, 27) by more frequently sam-
pling mutations that enhance functionality.
Here, we introduce a tuning mechanism for gene networks in
Escherichia coli that couples the straightforward tunability of
translation initiation rates via the RBS spacer region (18, 28,
29) with the high mutation rate and strong bias for insertion/
deletion mutations inherent to simple sequence repeats (SSR)
(30). We implement this mechanism by embedding mono- or di-
nucleotide SSRs between the Shine–Dalgarno sequence and the
start codon of target genes. We call this sequence motif the
We describe multiple methods to generate libraries of rbSSR
sequences that vary in repeat number, using them to evaluate this
tuning approach against the criteria above. We found that these
libraries incrementally and predictably sample gene expression
levels over a 1,000-fold range, and that the range of expression
can be expanded by coarsely tuning promoter strength. We de-
monstrate the utility of the approach by fine-tuning three func-
tional behaviors of a bistable switch built with dual rbSSRs, and
illustrate the need for tuning by showing that the genomic context
of a host strain can have profound effects on the switch’s beha-
vior. We also show that rbSSR sequences are stable over more
than 200 generations, but that destabilization of the repeats in
a mutator strain focuses mutations to the spacer region, which
could be used to tune and select for optimized gene networks
in vivo. These results are broadly applicable to rapidly engineer-
ing functional gene circuits and scaling up circuit complexity by
enabling the creation of expression libraries that thoroughly and
predictably sample the parameter space of a gene network.
Results and Discussion
Explorability and Predictability of rbSSRs. To understand the resolu-
tion and limits of translational control with rbSSRs, we experi-
mentally examined four rbSSR spacer motifs: ðAÞn, ðTÞn,
ðATÞn, and ðACÞn; that is, n repeats of either a single or a pair
of nucleotides. For each motif, we constructed a parent plasmid
with a constitutive promoter, a strong Shine–Dalgarno region,
and an initial rbSSR spacer, driving the expression of a gfp gene
(Fig. 1A). Taking advantage of the inherent instability of repeats
during replication, especially in PCR (31), we generated plasmid
libraries by amplifying a region of each parent plasmid flanking
the rbSSR sequence and re-inserting the mutated amplicons into
pre-cut plasmid backbones (Fig. S1A). The resulting plasmid li-
braries were transformed into E. coli and screened visually and
Author contributions: R.G.E. and E.K. designed research; R.G.E. performed research; R.G.E.
analyzed data; and R.G.E. and E.K. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1To whom correspondence should be addressed. E-mail: email@example.com.
This article contains supporting information online at www.pnas.org/lookup/suppl/
www.pnas.org/cgi/doi/10.1073/pnas.1205693109PNAS Early Edition
1 of 6
via cytometry for unique fluorescence levels to produce a strain
library (rbSSR-GFP) of repeat lengths for the four spacer motifs.
We measured the fluorescence output of rbSSR-GFP library
strains via flow cytometry of exponentially growing cells. The
mean intensity decreased linearly in log fluorescence over a
100- to 1,000-fold range as the number of rbSSR repeats in-
creased (Fig. 1B). The cell-cell variation in GFP levels for each
strain is considerably more uniform than what is observed for
more noise-prone tuning approaches, such as the dose-response
of an inducible promoter (32) (Fig. S2). The rate at which fluor-
escence intensity decreases depends on the nucleotide composi-
tion of the spacer, with the steepest decline for ðAÞnand the most
gradual decline for ðTÞn(Fig. 1C). The overall trend of the de-
cline roughly corresponds to computational predictions (18), but
with increasing disparity as the nucleotide composition of the
spacer deviates from poly-(A) residues (Fig. S3).
Creating rbSSR libraries is compatible with existing combina-
torial or compuational tuning approaches for transcription (12,
17) and translation (18, 19) rates, as well as for RNA stability
(21), and results in efficient sampling of the expression space with
a predictable mapping from sequence to expression. By coupling
rbSSRs with promoters of different strengths or by altering the 5′
untranslated region (UTR) of the transcript, different regions of
the expression space spanning nearly five orders of magnitude
can be sampled (Fig. 1D). Through PCR mutagenesis of the
rbSSR, we generated no fewer than nine bases of repeat se-
quence, which—depending on the nucleotide composition of the
RBS—results in spacing near the optimum of five bases (29)
that rbSSRs can be used to fine-tune functional gene networks,
we built an rbSSR-enhanced bistable switch (Fig. 2A) using the
same architecture as the mutually inhibitory switch described
by Gardner, Cantor, and Collins (33). For our switch, rbSSRs se-
parately drive the expression of the repressor proteins LacI
and TetR, which are expressed bicistronically with GFP and RFP,
The dominant state and spontaneous switching rate between
states for this circuit depend on the initial state of the system,
the expression strength of each repressor gene, the stability of
the associated proteins and mRNAs, the rate of leaky transcrip-
tion for each repressor/promoter combination (see Fig. S4), plas-
mid copy number, and circuit-host interactions that affect global
expression dynamics and growth rate. As a result, it is difficult to
predict a priori if one state will dominate or if each state will be
equally likely when the switch is expressed in a given strain. In
fact, our first assays with this switch architecture, untuned and
expressed in E. coli strain DH5α, showed that the switch was only
stable in one state: Despite an initial bias to the LacI-dominant
state, cells that spontaneously switched to the TetR-dominant
state eventually swept the population due to a growth rate advan-
tage (Fig. S5).
Depending on the values of the network parameters, the mu-
tual inhibition circuit architecture can produce a range of useful
behaviors. If both states are highly stable, cells can act as mole-
cular detectors, responding to environmental signals and retain-
ing the detected state over tens of generations in the absence of
the signal (33). If one state is sufficiently less stable than the
other, cells can act as programmable timers when initialized in
the less stable state (12). If the two switch states are well-
balanced, cells could perform coin-flipping behaviors as a noisy
switch. A synthetic bistable switch encoding coin-flipper beha-
viors could be a useful foundational circuit for implementing syn-
thetic multicellular systems that emerge from individual cells,
such as bet-hedging, pattern formation, and division of labor (34).
To make our switch easily tunable, we incorporated poly-(T)
and poly-(A) rbSSRs between identical, unoptimized 5′ UTR se-
quences and the tetR and lacI genes, respectively. From this de-
sign we built a 36-strain combinatorial library (rbSSR-BSS) using
oligo assembly (35) (Fig. S1C) of six single-stranded DNA frag-
ments that encode the regulatory sequences for the two operons,
Spacer with simple
Aligned spacing (nucleotides)
Aligned spacing (nucleotides)
1x promoter, as in (C)
1x promoter with
alternate 5’ UTR
GFP fluorescence (AU)
ribosome binding site between the Shine–Dalgarno sequence and the coding sequence of the target gene, in this case gfp. (B) Fluorescence distributions of the
ðATÞ6–ðATÞ12rbSSR-GFP library expressed from constitutive promoter J23100 and measured by flow cytometry. Varying the spacer length evenly samples a
large expression range over three orders of magnitude with uniform noise properties. (C) Mean GFP expression levels for rbSSR-GFP libraries generated from
parent plasmids ðAÞ20, ðTÞ20, ðATÞ12, and ðACÞ8(Table S4). Increasing the spacer length generally lowers gene expression, though the trend of the decline is
sensitive to the nucleotide composition of the repeats. Each library uses identical promoter (J23100) and 5′ UTR sequences. Error bars represent standard error
from three colonies. (D) Mean GFP expression levels for multiple ðAÞnrbSSR-GFP libraries, as in C. Tuning translational and transcriptional efficiencies through
regulatory sequences in the 5′ UTR or promoter regions (Table S5), respectively, can be used in conjunction with rbSSR libraries to more broadly sample the
The rbSSR construct and rbSSR-GFP library characterization. (A) The rbSSR construct. A simple sequence repeat is embedded in the spacer region of the
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www.pnas.org/cgi/doi/10.1073/pnas.1205693109Egbert and Klavins
including rbSSR spacers of length 10 to 20 repeats in steps of two
(Fig. 2A). This library represents a coarse sampling (30%) of the
reachable rbSSR expression space from 10 to 20 repeats. We
transformed sublibrary assemblies that contained all six poly-
(A) variants for each poly-(T) rbSSR into lacI−expression strain
2.320 for screening via flow cytometry and sequence verification.
We initially characterized the behaviors of the rbSSR-BSS
strain library by observing the natural bias of each switch variant
in fresh, uninduced 2.320 transformants. We used flow cytometry
to capture a scatter plot of red and green fluorescence for the
cells during exponential growth (Fig. 2B). We combined these re-
sults to produce a grid of scatter plots for the entire library
(Fig. 2C) and observed a suprisingly broad range of switch distri-
butions. As expected, a strong rbSSR for one repressor coupled
with a weak rbSSR for the opposing repressor results in a natural
bias toward the strong rbSSR state. However, the distribution of
switch states is bimodal for the majority of the library variants,
and the fraction of cells in the green state, while consistent for
a single strain, shifts predictably as rbSSR strength is varied.
Context Matters. The context in which a gene network such as the
bistable switch is expressed can dramatically affect the perfor-
mance of the network (4, 36), but its effect is, in general, poorly
understood and not easily incorporated into modeling gene net-
work performance. To illustrate this point, we transformed each
variant of the rbSSR-BSS library into a second lacI−strain of
E. coli—BW25113 ΔlacI—and performed the same initial char-
acterization assay described above. While the genotypes of these
two strains differ (see SI Text), one might expect the two to be
functionally identical with regard to the circuit’s behavior because
neither strain expresses LacI or TetR from the genome. In fact,
the behaviors of the second strain, shown in Fig. 2D, are pro-
foundly different. The same rbSSR pairs that generate bimodality
in the original strain generally produce unimodal behaviors in the
new strain. Also, while we observed little colony-to-colony varia-
tion in the distributions of switch states for the first strain library,
nearly half of the strains in the second library exhibited two dis-
tinct colony phenotypes—one biased toward LacI-dominant cells,
and the other biased toward TetR-dominant cells. For these
strains, the distribution of colony types shifted predictably with
rbSSR strength (Fig. S6).
To further characterize the behavior of the switch library, we
performed induction and microscopy assays on subsets of the
strain libraries. First, to observe the long-term stability of each
switch, we forced strains to the TetR- and LacI-dominant states
using the chemical inducers IPTG and aTc, respectively. After a
period of induced growth, we washed the inducers away and mon-
itored fluorescence distributions over 96 h of continuous growth
(Fig. S7). We found that construct ðTÞ12∕ðAÞ12in both strain
backgrounds exhibits robust bistable behaviors. In strain 2.320,
the bistability is maintained for the duration of the experiment.
For the weaker rbSSR pairing ðTÞ16∕ðAÞ14, the LacI-dominant
state is less robust, leading to mixed or TetR-dominant popula-
tions after 96 h. On the other hand, strains we found to have
low growth rates when in the naturally dominant state
(Table S1) behaved like monostable timer circuits, completely
switching state after initialization to the slow-growth state. Final-
ly, to investigate the switching dynamics of the uninduced switch,
we performed microcolony growth assays from single cells for a
subset of the 2.320 strain library (Table S2). Along the diagonal
for roughly equal rbSSR lengths, we observed a few rbSSR pair-
ings with a significant fraction of cells in transition between states,
which resulted in mixed-state microcolonies (Fig. S8 and
99.7±0.0 99.6±0.197.6±1.2 86.7±4.273.9±2.540.7±1.4
lacI rbSSR repeats, (A)n
tetR rbSSR repeats, (T)
lacI rbSSR repeats, (A)n
tetR rbSSR repeats, (T)
BW25113 ∆lacI strain library
2.320 strain library
upstream from tetR and ðAÞjrbSSR upstream from lacI repressor genes. A combinatorial library was built using oligos that encode different length rbSSR
sequences. (B) Scatter plot showing fluorescence distribution of ðTÞ20∕ðAÞ20rbSSR-BSS variant in strain 2.320. The horizontal axis is the GFP fluorescence level,
and the vertical axis is the RFP fluorescence level. The color indicates whether the cell is in the red, green, or mixed subpopulation, and the number represents
the mean and standard error of the percent of cells in the green state, measured from three colonies. Each scatter plot displays 1,000 points selected at random
from the associated cytometry data. (C) Grid of scatter plots for the 2.320 strain library plotted as in B. The comparative strengths of rbSSR pairs determine the
distribution of cells in the LacI- and TetR-dominant states. (D) Grid of scatter plots for the BW25113 ΔlacI strain library showing the fluorescence distributions of
the majority colony type. The host strain affects the behavior of the switch library primarily by sharpening the boundary between strains in which one or the
other state dominates and results in fewer bimodal constructs.
Construction of rbSSR-BSS library and its characterization in two lacI−strains. (A) The mutually inhibitory bistable switch architecture with ðTÞirbSSR
Egbert and KlavinsPNAS Early Edition
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The difference between Fig. 2 C and D and the range of func-
tional behaviors observed when varying rbSSR pairings both un-
derscore the importance of tuning and highlight limitations of
tuning a single network parameter. In the initial characterization
assay, we estimate roughly 40 generations pass from the initial
plating of transformants to the cytometry assay. Over that period,
switch variants in strain 2.320appear to approach steady-state dis-
tributions relatively quickly, evidenced by consistent fluorescence
distributions across colonies. By contrast, the same constructs in
BW25113 lacI−appear to maintain some initial state, resulting in
two distinct colony phenotypes. For this circuit, it is likely that
adjusting an additional network parameter, such as circuit copy
number (37) or promoter leak (17) could compensate for the con-
textual differences between the two strains. Although it is unlikely
that these differences could be predicted for two arbitrary strains,
it is reasonable to expect that two arbitrary strains could be tuned
to behave the same way—suggesting that building tuning into a
system is an extremely important design consideration.
Stability and Evolvability of rbSSRs. Although we are not aware of
ural context, these repeat sequences are known to accelerate evo-
lutionary adaptation when situated within coding sequences and
other regulatory regions (30). SSRs undergo insertion/deletion
mutations at rates four to five orders of magnitude higher than
arbitrary sequences of the same length (38, 39). Some bacteria
utilize SSR variability to strictly control protein expression via fra-
meshift mutations in coding sequences or to alter transcription
rates via insertions or deletions to promoter spacers (40). Repeats
embedded in promoter and gene coding sequences are also found
to be responsible for environmental adaptations in many higher
organisms (30). When used in synthetic gene circuits, the instabil-
ity of SSRs could be detrimental if it caused the performance of a
circuit to degrade or detune over time. On the other hand, repeat
variation could be a powerful tool to optimize circuit performance
through directed evolution by focusing mutations to regions of the
circuit that havestrongandpredictableeffects ongeneexpression.
To examine the long-term stability of rbSSR sequences, we
measured the sequence drift of the rbSSR-GFP gene circuit using
DNA sequence trace analysis of serially-passaged wild-type and
mutator strains over approximately 220 generations. Specifically,
we transformed the ðAÞ15rbSSR-GFP plasmid into wild-type
strain BW25113 and mismatch-repair deficient strain BW25113
ΔmutS, and cultured each strain in triplicate over 16 serial pas-
sages. We extracted plasmid DNA from each overnight culture
for sequence trace analysis (see Fig. S9). For the wild-type strain,
we observed no mutations in the gfp gene, including the regula-
tory and rbSSR sequences (Fig. 3A), which demonstrates that
rbSSR sequences can remain stable for very long periods. How-
ever, when propagated in the mutator strain, we observed a
strong bias for SSR deletions as the fraction of ðAÞ14plasmids
increased steadily over time (Fig. 3B).
By fitting a model of insertion/deletion mutations based on a
birth-death process to the data (Fig. 3 C and D and SI Text), we
inferred the mutation rates to be 2.6 × 10−4deletions and 5.1 ×
10−5insertions per base pair of repeat sequence per generation,
which are within the range of reported rates (38). We also in-
ferred the SSR mutation rate in the mutator strain to be at least
20 times greater than in the wild-type strain, which is consistent
with previous work (39). While we observed no mutations in the
promoter or the gfp coding sequence for the mutator strain, we
did find that rbSSR instability varied among replicates (Fig. S10)
with one replicate resulting in a final distribution of plasmids with
repeats ðAÞ13through ðAÞ17. Note that these experiments were
performed without any intentional selection, and the results
are likely primarily due to drift.
The experiments described in this report suggest that a complex
gene network may require substantial tuning to function as de-
sired. Our approach to tuning synthetic bacterial gene networks
uses a very simple construct: A sequence repeat in the spacer re-
gion of the RBS. Sequence repeats seem ideal for this purpose for
a variety of reasons. First, the relationship between the length of
the repeat and the strength of the resulting RBS is clear. Second,
the range of expression obtained by coupling rbSSR libraries with
other regulatory sequences, such as promoters, is large. Third,
genetic instability can be focused on the RBS spacer, allowing
. . .. . .
150 200 250
rbSSR drift (WT)
rbSSR drift (∆mutS)
1x: µ = 2.6 x 10 , λ = 5.1 x 10
generation for strain BW25113. The distribution of rbSSR repeats was essentially unchanged after 220 generations. Curves are least-absolute-deviations fits
to the model in C. (B) Distributions as in A for strain BW25113 ΔmutS. The fraction of plasmids with the original construct steadily decreased as the fraction
of plasmids with a single-unit deletion increased. The fraction of plasmids with a single-unit insertion also increased, though at a slower rate. (C) Birth–death
model ofSSR variation.Theprobability of repeat unit insertionis nλ andrepeat unitdeletion isnμ per repeat unitper generation,given a replication template of
repeat unit length n. The probability ϵ that two deletions or insertions occur in one generation is assumed to be zero. (D) Predictions for the fraction of ðAÞ15
plasmids over time for multiplemutation rates, with 1× corresponding to the rates obtained from a fit of the model in C to the datain B. Mutation rates of 0.05×
or below fit the data in A for the wild-type strain, suggesting that the mutator strain is at least 20 times more likely than wild-type to insert or delete a repeat.
The stability of rbSSR sequences in vivo. (A) Distributions of ðAÞ13to ðAÞ17rbSSR repeats in the plasmid population as a function of estimated cell
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www.pnas.org/cgi/doi/10.1073/pnas.1205693109Egbert and Klavins
rapid exploration of the expression space via PCRor combinator-
ial assembly methods. Additional experiments must be performed
to demonstrate the potential of these repeats as tools for tuning
gene networks in vivo via selection.
Our study also demonstrates that the range of behaviors of a
gene network library, in our case of a bistable switch, can be sub-
stantial and highly sensitive to the host context. We have shown
that cells expressing the switch can act as a molecular detector or
a timer, with possible applications in medical diagnostics or in-
dustrial bioprocessing. However, growth inhibition strategies
for the timer that rely on protein overexpression are not muta-
tionally robust, so other strategies may be required to increase
circuit stability. We have also shown that the same switch archi-
tecture, tuned properly, produces noisy coin-flipping behaviors
that could be used as a core circuit element to initiate cell differ-
entiation for synthetic multicellular systems.
We believe the same approach we have used to engineer highly
tunable elements with simple sequence repeats can be extended
to other network parameters in bacteria (40, 41) and to higher
organisms (42) by tuning the spacing between known regulatory
motifs such as those responsible for transcription initiation (12)
or intron splicing efficiency (43). To continue scaling up function-
ally complex behaviors in synthetic gene networks, these ap-
proaches will likely need to be combined with tuning methods
that control parameters that are untunable by our methods, in-
cluding network connectivity, protein-protein interactions, and
enzyme catalytic efficiencies. The present tuning approach will
likely be a part of comprehensive strategies for fine-tuning gene
circuits to perform optimally in a given context.
Materials and Methods
Strains and Media. GFP library construction and assays were carried out in
MG1655. Bistable switch library constructs were screened in 2.320 [Coli
Genetic Stock Center (CGSC) accession number 6440] and assayed in strains
2.320 and BW25113 ΔlacI. Serial passage experiments for in vivo mutation
rate analysis were carried out in wild-type BW25113 (CGSC accession number
7636) and mutator BW25113 ΔmutS strains. Strains BW25113 ΔlacI and
BW25113 ΔmutS were created from JW0336-1 (CGSC accesstion number
8528) and JW2703-2 (CGSC accession number 10126), respectively, using plas-
mid pFLP2 (44) to excise the FRT-flanked kanamycin cassettes, followed by
sucrose counterselection to cure the pFLP2 plasmid (45). M9 minimal media
(M8000, Teknova) supplemented with 50 μg∕mL kanamycin was used for
growth and fluorescence assays. Serial passages were performed in LB broth
(10 g∕L tryptone, 5 g∕L yeast extract, 10 g∕L NaCl) supplemented with
50 μg∕mL kanamycin.
GFP Library Generation. The backbone for the rbSSR-GFP libraries was gener-
ated from a parent plasmid with a p15A replication origin containing ðAÞ20,
ðTÞ20, ðACÞ8, or ðATÞ12by digestion with endonucleases XbaI and NdeI to ex-
cise a small region including the rbSSR, followed by gel extraction and pur-
ification. Spacer variation for the rbSSR-GFP libraries was generated by PCR
with Phusion Flash master mix (F548, Finnzymes) using the parent plasmids as
templates. PCR amplicons were purified by gel extraction. The backbone and
rbSSR amplicon libraries were joined using Gibson assembly (46), and trans-
formed directly into expression strain MG1655 for screening. Repeat lengths
with fewer than nine base pairs were not observed from PCR reactions; plas-
mids for repeats ðAÞ6–ðAÞ9(see Fig. S3A) were thus constructed by ordering
oligos encoding each spacer, followed by PCR amplification and Gibson as-
sembly to a PCR-amplified vector backbone. All constructs were verified
by sequencing. A more detailed description of the method is found in the
Bistable Switch Library Generation. The backbone for rbSSR-BSS library con-
struction was prepared from a parent plasmid with a p15A replication origin
by digestion with endonucleases AclI and SnaBI, followed by gel extraction
and purification. A set of six primers—O1R, O2F, O3R, O4F, O5R, and O6F (see
Table S3)—was ordered (IDT) to encode the regulatory region and introduce
rbSSR sequences. Six variant oligos encoding rbSSRs ðTÞ10;12;…;20 and
ðAÞ10;12;…;20were ordered for primers O1R and O6F, respectively. A short
PCR amplicon from tetR was used to join the digested backbone to the oligos.
Combinatorial libraries of all poly-(A) repeats for each poly-(T) repeat were
generated using Gibson assembly with the backbone fragment, the PCR
amplicon, and the proper mix of oligos, and transformed directly into expres-
sion strain 2.320 for screening. The regulatory regions between digestion
sites for each library clone were verified by sequencing. A more detailed de-
scription of the method is found in the SI Text.
Cell Growth and Plate Reader Measurements. For GFP assays, freshly streaked
colonies were transferred in triplicate to 200 μL M9 minimal media in 96-well
plates (Costar 3795) and grown to saturation overnight at 37°C in a shaker.
The cultures were then diluted 1∶100 in 200 μL prewarmed fresh broth in 96-
well plates (Costar 3904), grown at 37°C to OD600 0.15 to 0.2 in a plate reader
(Biotek) with shaking. Optical densities (600 nm) and GFP measurements (485-
nm excitation, 510-nm emission) were taken every 10 min. When grown to
target density, 10 μL of each culture was transferred to 100 μL 1× PBS (Gibco)
with 34 μg∕mL chloramphenicol chilled at 4°C. Bistable switch assays were
performed similarly, with the exception that freshly transformed colonies
were selected at random from plates after incubation for 12–14 h at 37°C,
and fluorescence measurements were made in the plate reader for RFP
(590-nm excitation, 632-nm emission) in addition to optical density and GFP.
Flow Cytometry Measurements. Diluted cultures from the plate reader mea-
surements were transferred to a flow cytometer (C6 with CSampler, Accuri).
To prevent well–well contamination, blank wells containing PBS were read
after each sample well. GFP measurements (488-nm excitation, 533-nm emis-
sion) and RFP measurements (488-nm excitation, 610-nm emission) were re-
corded for 50,000 events per sample. Cells were gated using a rectangular
gate in forward scatter and side scatter. For the GFP assays, background fluor-
escence levels from cells containing an empty vector without gfp were sub-
tracted from the geometric mean of GFP expression for each sample culture.
Fluorescence levels in both the GFP and RFP channels were used to generate
the scatter plots for the bistable switch assays. Cytometry measurements
were analyzed using custom Matlab scripts (see SI Text). For color compensa-
tions, 4.5% of the red channel fluorescence was subtracted from the green
channel fluorescence, and 7.8% of the green channel fluorescence was sub-
tracted from the red channel fluorescence. These percentages were deter-
mined using experimental data from strains with plasmids expressing only
RFP or GFP, respectively. Cutoffs from color-compensated levels of 500 and
750 were used for classifying cells as “red” or “green,” respectively (see
rbSSR Variation in Vivo. Freshly transformed colonies of wild-type and muta-
tor strains containing the ðAÞ15rbSSR-GFP plasmid were transferred in tripli-
cate to 6 mL of LB supplemented with kanamycin and grown to saturation
overnight at 37°C in a shaker. Overnight cultures were mixed and 1 μL trans-
ferred into 6 mL fresh broth, which was grown to saturation. Plasmid mini-
preps from overnight cultures were prepared, sequenced, and the
sequencing traces were analyzed as described below. This process was fol-
lowed through 16 passages.
Chromatogram Processing of in Vivo rbSSR Passages. Chromatogram
trace data from sequencing reactions were converted to csv files with the
abiparser.py Python script (http://www.bioinformatics.org/groups/?group_
id=497) and imported into Mathematica for analysis. Trace data were sepa-
rated by nucleotide identity into individual channels and four isolated
bases—A30, T99, T115, and G143—were selected for calculating repeat distribu-
tions. Each isolated nucleotide was located at least five bases from the nearest
nucleotide of the same type (Fig. S9). For each isolated nucleotide, peak
heights over a 7-peak window were normalized to calculate a distribution
of repeats ðAÞ13to ðAÞ17. The mean of these four distributions was used to
populate the serial passage dataset.
Note that we consistently observed an apparent 5–8% contribution from
a single-unit deletion for the ðAÞ15sequence traces. This noise, possibly an
artifact of polymerase slippage in the sequencing reaction, was observed
in all rbSSR ðAÞ15sequencing traces, including the initial sequence verification
of the ðAÞ15rbSSR-GFP construct and all passages of the wild-type strain
across three replicates, with a mean contribution of 6.6% (n ¼ 50). To com-
pensate for this noise we adjusted the peak height Pnfor repeat length n to
^Pn¼ Pn− 0.066Pnþ1 for each target nucleotide when computing distri-
ACKNOWLEDGMENTS. The authors thank Georg Seelig for helpful discussions
and advice on experiments and the manuscript, and David Thorsley for
discussions on modeling mutation rates. This work was supported by the
National Science Foundation (NSF) through the Molecular Programming
Project (Grant 0832773) and an NSF Graduate Research Fellowship (R.G.E.).
Egbert and KlavinsPNAS Early Edition
5 of 6
1. Rosenfeld N, Young JW, Alon U, Swain PS, Elowitz MB (2005) Gene regulation at the
single-cell level. Science 307:1962–1965.
2. Süel GM, Kulkarni RP, Dworkin J, Garcia-Ojalvo J, Elowitz MB (2007) Tunability and
noise dependence in differentiation dynamics. Science 315:1716–1719.
3. Arkin AP, Fletcher DA (2006) Fast, cheap and somewhat in control. Genome Biol 7:114.
4. Danchin A (2012) Scaling up synthetic biology: Do not forget the chassis. FEBS Lett
5. Lu TK, Khalil AS, Collins JJ (2009) Next-generation synthetic gene networks. Nat
6. Purnick PE, Weiss R (2009) The second wave of synthetic biology: From modules to
systems. Nat Rev Mol Cell Biol 10:410–422.
7. Fischbach M, Voigt CA (2010) Prokaryotic gene clusters: A rich toolbox for synthetic
biology. Biotechnol J 5:1277–1296.
8. O’Shaughnessy EC, Palani S, Collins JJ, Sarkar CA (2011) Tunable signal processing in
synthetic MAP kinase cascades. Cell 144:119–131.
9. Murphy K, Adams R, Wang X, Balazsi G, Collins J (2010) Tuning and controlling gene
expression noise in synthetic gene networks. Nucleic Acids Res 38:2712–2726.
10. Callura J, Dwyer D, Isaacs F, Cantor C, Collins J (2010) Tracking, tuning, and terminating
microbial physiology using synthetic riboregulators. Proc Natl Acad Sci USA
11. Babiskin AH, Smolke CD (2011) A synthetic library of RNA control modules for predict-
able tuning of gene expression in yeast. Mol Syst Biol 7:471.
12. Ellis T, Wang X, Collins JJ (2009) Diversity-based, model-guided construction of
synthetic gene networks with predicted functions. Nat Biotechnol 27:465–471.
13. Wang HH, et al. (2009) Programming cells by multiplex genome engineering and
accelerated evolution. Nature 460:894–898.
14. Yokobayashi Y, Weiss R, Arnold FH (2002) Directed evolution of a genetic circuit. Proc
Natl Acad Sci USA 99:16587–16591.
15. Bashor CJ, Horwitz AA, Peisajovich SG, Lim WA (2010) Rewiring cells: Synthetic biology
as a tool to interrogate the organizational principles of living systems. Annu Rev
16. Alper H, Fischer C, Nevoigt E, StephanopoulosG (2005) Tuning geneticcontrol through
promoter engineering. Proc Natl Acad Sci USA 102:12678–12683.
17. Cox RS, Surette MG, Elowitz MB (2007) Programming gene expression with combina-
torial promoters. Mol Syst Biol 3:145.
18. Salis HM, Mirsky EA, Voigt CA (2009) Automated design of synthetic ribosome binding
sites to control protein expression. Nat Biotechnol 27:946–950.
19. Mutalik VK, Qi L, Guimaraes JC, Lucks JB, Arkin AP (2012) Rationally designed families
of orthogonal RNA regulators of translation. Nat Chem Biol 8:447–454.
20. Pfleger BF, Pitera DJ, Smolke CD, Keasling JD (2006) Combinatorial engineering of
intergenic regions in operons tunes expression of multiple genes. Nat Biotechnol
21. Carothers JM, Goler JA, Juminaga D, Keasling JD (2011) Model-driven engineering of
RNA devices to quantitatively program gene expression. Science 334:1716–1719.
22. Andersen JB, et al. (1998) New unstable variants of green fluorescent protein for stu-
dies of transient gene expression in bacteria. Appl Environ Microbiol 64:2240–2246.
23. Poelwijk FJ, de Vos MGJ, Tans SJ (2011) Tradeoffs and optimality in the evolution of
gene regulation. Cell 146:462–470.
24. Siegel JB, et al. (2010) Computational design of an enzyme catalyst for a stereoselec-
tive bimolecular Diels-Alder reaction. Science 329:309–313.
25. Feng XJ, et al. (2004) Optimizinggenetic circuits by global sensitivityanalysis. Biophys J
26. Wong TS, Zhurina D, Schwaneberg U (2006) The diversity challenge in directed protein
evolution. Comb Chem High Throughput Screening 9:271–288.
27. Haseltine EL, Arnold FH (2007) Synthetic gene circuits: Design with directed evolution.
Annu Rev Biophys Biomol Struct 36:1–19.
28. Vellanoweth RL, Rabinowitz JC (1992) The influence of ribosome-binding-site
elements on translational efficiency in Bacillus subtilis and Escherichia coli in vivo.
Mol Microbiol 6:1105–1114.
29. Chen H, Bjerknes M, Kumar R, Jay E (1994) Determination of the optimal aligned
spacing between the Shine-Dalgarno sequence and the translation initiation codon
of Escherichia coli mRNAs. Nucleic Acids Res 22:4953–4957.
30. Gemayel R, Vinces MD, Legendre M, Verstrepen KJ (2010) Variable tandem repeats
accelerate evolution of coding and regulatory sequences. Annu Rev Genet
31. Shinde D, Lai YL, Sun FZ, Arnheim N (2003) Taq DNA polymerase slippage mutation
rates measured by PCR and quasi-likelihood analysis: ðCA∕GTÞnand ðA∕TÞnmicrosa-
tellites. Nucleic Acids Res 31:974–980.
32. Nevozhay D, Adams RM, Murphy KF, Josic K, Balázsi G (2009) Negative autoregulation
linearizes the dose-response and suppresses the heterogeneity of gene expression.
Proc Natl Acad Sci USA 106:5123–5128.
33. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in
Escherichia coli. Nature 403:339–342.
34. Eldar A, Elowitz MB (2010) Functional roles for noise in genetic circuits. Nature
35. Gibson DG, Smith HO, Hutchison CA, Venter JC, Merryman C (2010) Chemical synthesis
of the mouse mitochondrial genome. Nat Methods 7:901–903.
36. Bagh S, et al. (2008) Plasmid-borne prokaryotic gene expression: Sources of variability
and quantitative system characterization. Phys Rev E 77:021919.
37. Loinger A, Biham O (2009) Analysis of genetic toggle switch systems encoded on plas-
mids. Phys Rev Lett 103:068104.
38. Torres-Cruz J, van der Woude MW (2003) Slipped-strand mispairing can function as a
phase variation mechanism in Escherichia coli. J Bacteriol 185:6990–6994.
39. Levinson G, Gutman GA (1987) High frequencies of short frameshifts in poly-CA/TG
tandem repeats borne by bacteriophage M13 in Escherichia coli K-12. Nucleic Acids
40. van Belkum A, Scherer S, van Alphen L, Verbrugh H (2006) Bacterial contingency loci:
The role of simple sequence DNA repeats in bacterial adaptation. Annu Rev Genet
41. Müller J, Oehler S, Müller-Hill B (1996) Repression of lac promoter as a function of
distance, phase and quality of an auxiliary lac operator. J Mol Biol 257:21–29.
42. Wierdl M, Greene CN, Datta A, Jinks-Robertson S, Petes TD (1996) Destabilization of
simple repetitive DNA sequences by transcription in yeast. Genetics 143:713–721.
43. Chua K, Reed R (2001) An upstream AG determines whether a downstream AG is
selected during catalytic step II of splicing. Mol Cell Biol 21:1509–1514.
44. Hoang TT, Karkhoff-Schweizer RR, Kutchma AJ, Schweizer HP (1998) A broad-
host-range Flp-FRT recombination system for site-specific excision of chromoso-
mally-located DNA sequences: Application for isolation of unmarked Pseudomonas
aeruginosa mutants. Gene 212:77–86.
45. Baba T, et al. (2006) Construction of Escherichia coli K-12 in-frame, single-gene knock-
out mutants: The Keio collection. Mol Syst Biol 2:2006.0008.
46. Gibson DG, et al. (2009) Enzymatic assembly of DNA molecules up to several hundred
kilobases. Nat Methods 6:343–345.
6 of 6
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