Negative auto-regulation increases the input dynamic-range of the arabinose system of Escherichia coli.
ABSTRACT Gene regulation networks are made of recurring regulatory patterns, called network motifs. One of the most common network motifs is negative auto-regulation, in which a transcription factor represses its own production. Negative auto-regulation has several potential functions: it can shorten the response time (time to reach halfway to steady-state), stabilize expression against noise, and linearize the gene's input-output response curve. This latter function of negative auto-regulation, which increases the range of input signals over which downstream genes respond, has been studied by theory and synthetic gene circuits. Here we ask whether negative auto-regulation preserves this function also in the context of a natural system, where it is embedded within many additional interactions. To address this, we studied the negative auto-regulation motif in the arabinose utilization system of Escherichia coli, in which negative auto-regulation is part of a complex regulatory network.
We find that when negative auto-regulation is disrupted by placing the regulator araC under constitutive expression, the input dynamic range of the arabinose system is reduced by 10-fold. The apparent Hill coefficient of the induction curve changes from about n = 1 with negative auto-regulation, to about n = 2 when it is disrupted. We present a mathematical model that describes how negative auto-regulation can increase input dynamic-range, by coupling the transcription factor protein level to the input signal.
Here we demonstrate that the negative auto-regulation motif in the native arabinose system of Escherichia coli increases the range of arabinose signals over which the system can respond. In this way, negative auto-regulation may help to increase the input dynamic-range while maintaining the specificity of cooperative regulatory systems. This function may contribute to explaining the common occurrence of negative auto-regulation in biological systems.
- SourceAvailable from: Yili Zhang[Show abstract] [Hide abstract]
ABSTRACT: Cellular functions and responses to stimuli are controlled by complex regulatory networks that comprise a large diversity of molecular components and their interactions. However, achieving an intuitive understanding of the dynamical properties and responses to stimuli of these networks is hampered by their large scale and complexity. To address this issue, analyses of regulatory networks often focus on reduced models that depict distinct, reoccurring connectivity patterns referred to as motifs. Previous modeling studies have begun to characterize the dynamics of small motifs, and to describe ways in which variations in parameters affect their responses to stimuli. The present study investigates how variations in pairs of parameters affect responses in a series of ten common network motifs, identifying concurrent variations that act synergistically (or antagonistically) to alter the responses of the motifs to stimuli. Synergism (or antagonism) was quantified using degrees of nonlinear blending and additive synergism. Simulations identified concurrent variations that maximized synergism, and examined the ways in which it was affected by stimulus protocols and the architecture of a motif. Only a subset of architectures exhibited synergism following paired changes in parameters. The approach was then applied to a model describing interlocked feedback loops governing the synthesis of the CREB1 and CREB2 transcription factors. The effects of motifs on synergism for this biologically realistic model were consistent with those for the abstract models of single motifs. These results have implications for the rational design of combination drug therapies with the potential for synergistic interactions.PLoS Computational Biology 03/2014; 10(3):e1003524. · 4.87 Impact Factor
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ABSTRACT: Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.Biotechnology Journal 02/2014; · 3.45 Impact Factor
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ABSTRACT: Escherichia coli can uptake and utilize many common natural sugars to form biomass or valuable target bio-products. Carbon catabolite repression (CCR) will occur and hamper the efficient production of bio-products if E. coli strains are cultivated in a mixture of sugars containing some preferred sugar, such as glucose. Understanding the transport and metabolism mechanisms of the common and inexpensive sugars in E. coli is important for further improving the efficiency of sugar bioconversion and for reducing industrial fermentation costs using the methods of metabolic engineering, synthetic biology and systems biology. In this review, the transport and mediation mechanisms of glucose, fructose, sucrose, xylose and arabinose are discussed and summarized, and the hierarchical utilization principles of these sugars are elucidated.Biotechnology advances 04/2014; · 8.25 Impact Factor
RESEARCH ARTICLEOpen Access
Negative auto-regulation increases the input
dynamic-range of the arabinose system of
Daniel Madar, Erez Dekel, Anat Bren and Uri Alon*
Background: Gene regulation networks are made of recurring regulatory patterns, called network motifs. One of
the most common network motifs is negative auto-regulation, in which a transcription factor represses its own
production. Negative auto-regulation has several potential functions: it can shorten the response time (time to
reach halfway to steady-state), stabilize expression against noise, and linearize the gene’s input-output response
curve. This latter function of negative auto-regulation, which increases the range of input signals over which
downstream genes respond, has been studied by theory and synthetic gene circuits. Here we ask whether
negative auto-regulation preserves this function also in the context of a natural system, where it is embedded
within many additional interactions. To address this, we studied the negative auto-regulation motif in the
arabinose utilization system of Escherichia coli, in which negative auto-regulation is part of a complex regulatory
Results: We find that when negative auto-regulation is disrupted by placing the regulator araC under constitutive
expression, the input dynamic range of the arabinose system is reduced by 10-fold. The apparent Hill coefficient of
the induction curve changes from about n = 1 with negative auto-regulation, to about n = 2 when it is disrupted.
We present a mathematical model that describes how negative auto-regulation can increase input dynamic-range,
by coupling the transcription factor protein level to the input signal.
Conclusions: Here we demonstrate that the negative auto-regulation motif in the native arabinose system of
Escherichia coli increases the range of arabinose signals over which the system can respond. In this way, negative
auto-regulation may help to increase the input dynamic-range while maintaining the specificity of cooperative
regulatory systems. This function may contribute to explaining the common occurrence of negative auto-
regulation in biological systems.
Transcription regulation networks are largely made up
of recurring regulatory patterns called network motifs
[1-4]. These network motifs have been demonstrated to
carry out specific information-processing functions (e.g.
[1,3,5]). One of the simplest and most abundant net-
work motifs is negative auto-regulation (NAR). In this
motif, a transcription factor (TF) negatively regulates
the promoter of its own gene or operon [1,3,6] (Figure
1a). Approximately 40% of the known transcription fac-
tors in Escherichia coli show negative auto-regulation
, as do many transcription factors in yeast and higher
NAR has been suggested experimentally and theoreti-
cally to have several functions. The first is increased
homeostasis or buffering of the auto-regulated gene pro-
duct concentration against stochastic noise [12-14].
Because protein levels can vary from cell to cell by tens
of percents [15-17], such a noise buffering mechanism is
useful in cases where precision in TF levels is needed
. Low frequency noise in TF production rates tends
to be buffered by NAR because negative feedback
reduces TF levels if they are too high, and increases
them if they are too low, making TF levels more uni-
form across cells.
* Correspondence: email@example.com
Department of Molecular Cell Biology, The Weizmann Institute of Science,
Rehovot, 76100, Israel
Madar et al. BMC Systems Biology 2011, 5:111
© 2011 Madar et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
A second feature of NAR is its ability to speed the
response time of gene circuits [6,19,20]. Response time
is defined as the time it takes to reach half of the total
change in a dynamic process. Theoretical comparison
between NAR and a simply regulated promoter with no
NAR, with parameters in which both reach the same
steady-state level expression, shows that the response
time is shorter when the TF is negatively auto-regulated.
This speed up is achieved by the use of a strong promo-
ter allowing a rapid initial rise in TF levels, up to its
auto-repression threshold, followed by reduction in pro-
duction rate due to NAR . This speedup feature was
observed in a synthetic NAR circuit  as well as in
the native SOS DNA repair system of E. coli .
Speedup offered by NAR may be advantageous in
dynamic environments where rapid responses improve
Recently, it was shown by Nevozhay et al. that NAR
can also linearize dose responses . In this study the
response of synthetic, TetR-based transcriptional circuits
with and without NAR was studied in S. cerevisiae as a
function of inducer (anhydrotetracycline, aTc) levels.
NAR was found to transform a sigmoid induction curve
into a more linear curve (see also ). This feature was
also suggested in theoretical studies [6,22-25]. This role
of NAR can be interpreted as an increase in the input
dynamic range - the range of input signals over which
the system can respond.
Such theoretical and synthetic-circuit studies are a
powerful approach because one can study the function
of circuits such as NAR without of interfering effects. In
natural systems, however, this motif is embedded inside
a large regulatory network with many other interactions.
These additional interactions might in principle modify
its function. Therefore, to fully test the function of a
motif requires, in addition to theory and synthetic cir-
cuits, experiments on the motif in its natural context,
wired into the full interaction networks of the cell.
Here, we study the function of the NAR motif in a
natural system. We chose one of the best-studied gene
regulation systems, the arabinose utilization system of E.
coli. This system has been characterized over the past
decades by Schleif and colleagues ([26-29] for reviews).
The arabinose-responsive TF, called AraC, is negatively
autoregulated (Figure 1b). We asked whether NAR
increases the input dynamic range in this system.
The arabinose system is composed of 9 genes
arranged in 5 operons: araC- the system-specific TF;
araE, araFGH, araJ- the arabinose transporters [30-32];
and araBAD- arabinose catabolic enzymes. Two oper-
ons, araC and araBAD, are divergent and share the
same regulatory region (Figure 1d). The system is regu-
lated by cAMP Receptor Protein (CRP) and AraC (Fig-
ure 1e), which are activated by cAMP and L-Arabinose
respectively [26-29]. AraC represses its own promoter,
creating a NAR motif. It both activates and represses
the arabinose utilization operon araBAD by means of a
DNA looping mechanism [33,34]. AraC undergoes a
conformational change when it binds L-Arabinose, lead-
ing to expression of the ara genes. The system includes
several interactions and feedback loops, in which meta-
bolic enzymes and transporters downstream of araC
affect the level of intracellular arabinose, the inducer
that activates AraC (Figure 1e). In a study of the input
functions of E. coli sugar systems, it was recently found
that promoters from the arabinose system respond to
their inducer with a wider input dynamic range com-
pared to other sugar systems (eg. the maltose system) in
which the TF is not negatively auto-regulated .
To test the role of NAR, we compared the wild type
ara system (Figure 1b) to a variant in which NAR is dis-
rupted by placing the regulator AraC under a constitu-
tive promoter (Figure 1c). We find, using high-temporal
resolution measurement of promoter activity, that dis-
rupting NAR in the arabinose system increases the
steepness of the sigmoidal response curve. It reduces
the input dynamic range by about an order of magni-
tude. Thus, NAR increases input dynamic range in the
context of the natural ara system. We also analyze this
mathematically, suggesting that the increase in input
dynamic range is due to the increase of AraC protein
level with increasing arabinose due to the NAR.
The native input function of the araBAD promoter has a
broad input dynamic range
The input dynamic range is defined as the range of inputs
over which the output changes significantly. Operation-
ally, following Goldbeter and Koshland [36-38], we define
the input dynamic range as the ratio R of input levels at
which the system shows 90% and 10% of its maximal out-
put (Figure 2). For a Hill curve with coefficient n, the
input dynamic range is R = 811/n. Thus, Michaelis-Men-
ten like curves with n = 1 show R = 81, steeper sigmoidal
curves with n = 2 show R = 9, and very steep cooperative
curves with n = 4 show R = 3.
In order to determine the input dynamic range of E.
coli promoters we used a fluorescent reporter automated
assay [35,39], with strains from the comprehensive E.
coli transcription reporter library . Each strain bears
a low-copy plasmid with a green fluorescent protein
gene under the control of a full length copy of the pro-
moter of interest. In this study we used reporters for the
araBAD and araC promoters in E. coli strain MG1655
Reporter strains were grown on glucose minimal med-
ium containing saturating amount of cAMP (30 mM, to
fully activate CRP) and increasing amounts of L-
Madar et al. BMC Systems Biology 2011, 5:111
Page 2 of 9
Figure 1 An overview of the regulatory network of the arabinose utilization system in E. coli. (a). NAR motif with transcription factor X
that regulates its own production, and also regulates the production of gene Z (Z often represents several different downstream promoters). (b)
The araBAD genes are regulated by AraC which is negatively auto-regulated. (c) NAR in this study was disrupted by decoupling araC from its
native regulation. (d) The araC\araBAD divergent promoter structure. Colorless boxes are genes and colored boxes are the transcription factors’
binding sites: red- repressor; green- activator; brown- dual regulator (based on Ecocyc [50,51], see text for details). (e) The arabinose system is a
complex regulatory network in which NAR is only one of many interaction arrows, in which transporters and enzymes modify the intracellular
arabinose levels, which in turn acts to repress and activate the pumps and enzymes genes (see text for details).
Madar et al. BMC Systems Biology 2011, 5:111
Page 3 of 9
Arabinose [35,41]. Promoter activity (PA) was defined as
the rate of GFP production per OD (optical density)
unit, PA = dGFP/dt/OD (see Methods). The input func-
tions were derived from the promoter activity averaged
over a window that spans 1-2 cell generations in expo-
nential phase (5-7 hours after initial 1:600 inoculation).
Over this time window, promoter activity was constant
to a good approximation (see Additional File 1 for fluor-
escence and growth curves, p. 2-3, Figure S1 and S2
The promoter activity of the araBAD in the parental
strain (wild-type araC regulation, U424) as a function of
arabinose concentration is shown in Figure 3a. At low ara-
binose levels (below about 10 μM arabinose) the fluores-
cence of the reporter is indistinguishable from the cells
auto-fluorescence background. The input function reaches
10% of its maximal value at arabinose levels of about 0.1
mM, and 90% of its maximal value at about 10 mM. Fit-
ting a Hill curve to the input function results in an appar-
ent Hill coefficient of n = 1 ± 0.3 (s.e.), and halfway
induction point of K = 1.1 ± 0.4 mM (s.e.). The input
dynamic range is R = 100 ± 40 (s.e.). These results are
similar to measurements of the input function of the ara-
BAD reporter strain in wild-type MG1655 (U429) ,
and are consistent with the expected value for a curve
with Hill coefficient equal to n = 1, in which R = 81.
The araC gene is induced by arabinose
We also tested the dependence of the araC promoter
activity on arabinose. Since AraC negatively regulates its
own promoter, arabinose is expected to affect araC
Figure 2 Gene input function and its input dynamic range. The
input function is defined as the normalized promoter activity at
different signal concentrations. The black horizontal dashed lines
mark the 10% and 90% promoter activity. The input dynamic range
is the ratio R of input concentrations required for 90% and 10% of
Figure 3 The input dynamic range of the araBAD operon is
reduced by disrupting the NAR that controls araC. Shown is
araBAD promoter activity as a function of arabinose concentration.
The error bars indicate the s.e. (a) Promoter activity (dGFP/dt/OD,
arbitrary units) in the parental strain (U424, with NAR). The blue
squares are the experimental results. The dashed blue line is a fitted
Hill function with Hill coefficient n = 1 ± 0.3 (s.e.), K = 1.1 ± 0.4 mM
(s.e.), and R = 100 ± 40 (s.e.), and also the best fit solution of the full
model described in Additional File 1. The black horizontal dashed
lines mark the 10% and 90% promoter activity. (b) Promoter activity
(dGFP/dt/OD, arbitrary units) in the mutant strain (U426, without
NAR). The red circles are the experimental results. The solid red line
is a fitted Hill function with Hill coefficient n = 1.9 ± 0.4 (s.e.), K = 42
± 6 mM (s.e.), and R = 10 ± 3 (s.e.), and also the best fit solution of
the full model described in Additional File 1. The black horizontal
dashed lines mark the 10% and 90% promoter activity. (c)
Normalized promoter activity of the two strains. The x axis is the L-
Arabinose concentration divided by K per strain. This best
demonstrates differences in the input dynamic range (R) between
the two strains. Blue squares and dashed blue line are of the
parental strain (U424, with NAR), while red circles and solid red line
are of the mutant strain (U426, without NAR). Symbols are the
measured results and the lines are fitted Hill functions.
Madar et al. BMC Systems Biology 2011, 5:111
Page 4 of 9
expression. Indeed, using an araC reporter strain (U428),
we find that arabinose increases the activity of the araC
promoter in a dose-dependent manner (Figure 4) .
Disruption of negative auto-regulation of araC reduces
the input dynamic range of araBAD
To study the role of the negative auto-regulation of
araC on the input dynamic range of its downstream
genes, we decoupled araC expression from its negative
auto-regulation (Figure. 1c). For this purpose we deleted
the araC open reading frame from the chromosome of
the wild-type strain MG1655 and re-introduced araC on
a plasmid (pZE11) which provides constitutive expres-
sion (strain U426, see Methods). The plasmid has a tetR
controlled promoter, repressed by a chromosomal tetR
gene. Without induction, this plasmid produces levels of
AraC that are comparable to the induced wild-type
AraC level, as assessed from the maximal promoter
activity of the araBAD reporter. It should be noted that
the parental strain in this study (U424) also contains
chromosomal tetR as well as an emptly pZE11 vector, in
order to preserve genotypic identity between the two
We find that in the absence of NAR, the arabinose-
dependent input function of araBAD is significantly
steeper than the parental input function (Figure 3b,c),
with an apparent Hill coefficient of n = 1.9 ± 0.4 (s.e.),
and halfway induction point of K = 42 ± 0.6 mM (s.e.).
The measured input dynamic range spans between 14
mM - 135 mM, and thus has R = 10 ± 3 (s.e.), in com-
parison to R = 100 ± 40 (s.e.) in the parental strain.
Thus, decoupling araC from its negative auto-regulation
reduces the input dynamic-range of its downstream
genes by about an order of magnitude.
A model of NAR and increased input dynamic range
What is the main effect at play that allows negative
auto-regulation to increase input dynamic range? To
understand this, we analyzed a mathematical model of
the NAR motif. We sought to make the model as sim-
ple as possible, in order to be able to understand it
intuitively, and at the same time not too simple so as
not to lose the essence of the problem. A more compre-
hensive model, based on mass-action kinetics, which
includes a dual transcription factor that acts as both a
repressor and an activator, is given in Additional File 1
Consider a transcription factor whose concentration is
X, that binds its inducer s with a dissociation constant
Ks. The amount of X bound to s, which is the active
form of the transcription factor X*, is described by
The active transcription factor X* binds the promoter
of a downstream gene Z with Michaelis-Menten-like
kinetics, so that the steady-state level of the Z gene pro-
Where Kzis the dissociation constant of X*from the
promoter of Z, bZis the maximal production rate of Z,
and a is its degradation/dilution rate .
Without negative auto-regulation, the concentration of
X is independent of the inducer levels. We denote this
constant level X0. Using Eq. (1) in Eq. (2) with X = X0
results in a sigmoidal regulation function with an input
dynamic range of R = 9.
It is at this point that negative auto-regulation has an
important effect: instead of a constitutive level of X,
negative auto-regulation allows the signal s to modify
the concentration of X, an effect termed direct coupling
. With negative auto-regulation of the type found in
the ara system, the promoter that encodes X is
repressed by free X, (denoted Xf) a repression which is
relieved when X is bound to the signal.
Figure 4 The promoter activity of araC increases with
arabinose. Shown is araC promoter activity as a function of
arabinose concentration, in the wild-type strain (with NAR, green
circles). The dashed green line is a fitted Hill functions with n = 1 ±
0.6, K = 0.6 ± 0.4M (s.e.). The solid purple line is the best fit of the
full model described in Additional File 1.
Madar et al. BMC Systems Biology 2011, 5:111
Page 5 of 9
To analyze this, consider the rate of production of X
that is repressed by Xf, balanced by degradation/
dilution of the protein at rate a, so that:
Where Kxis the dissociation constant of X from its
own promoter, and the free X (Xf) is given by the
unbound fraction, Xf= X-X*:
Substituting Eq. (5) into Eq. (4) and assuming strong
binding of the regulator to its own promoter Kx<<Xf,
one finds that at steady-state the level of X increases as
the square root of the input signal s:
Where A2= Kxbx/a. In other words, the transcription
factor (X) levels increases with the signal (s) levels (see
the relationship between AraC and L-Arabinose in Fig-
Using this expression for X instead of X0in Eq. (2),
results in an input-function that is less steep, because of
the square-root dependence of X on s:
X = A
1 + s/Ks
. This function has an input
dynamic range of R = 81, which is 9 fold wider than
that of Eq. (3). Thus, NAR increases the input dynamic
Note that the assumption Kx<<Xfis not crucial for the
increased input dynamic range, and was used only for
the sake of simplicity. In Additional File 1 we present a
full mass-action model, without these assumptions, and
show that the present considerations apply as well.
We further investigated the effect of NAR on input
functions with different cooperativity in the binding of
the TF to the promoter, as described by Hill equations.
In the present system, the araBAD input function with-
out NAR has an apparent Hill coefficient of about 2,
suggesting that the AraC regulator is cooperative with n
= 2. In Figure 5 we describe the results of the model
with regulators with degrees of apparent cooperativity of
the regulator ranging between n = 1 and n = 5. It is
seen that NAR increases the input dynamics range in all
cases. For example, at n = 2, the input dynamic range
without NAR is R = 9, but can reach up to R ~ 1000
with NAR (the values observed above for the ara system
are about R = 10 without NAR and R = 100 with NAR).
Furthermore, the model explains how the change in
regulator levels caused by NAR can cause a shift in the
halfway induction point K of downstream genes (relative
to no NAR). The direction and size of the shift depends
on the mode of regulation. For repressors, K generally
increases with regulator levels, whereas activators show
the converse dependence. Since AraC both activates and
represses araBAD, the detailed model in Additional File
1 explains the observed increase in K shown in Figure 3
(Additional File 1, p.8-9, Figure S3).
To summarize the conclusions of this analysis, nega-
tive auto-regulation causes regulator levels to increase
with inducer level. This enhances the input dynamic
range by extending the range of inputs that can affect
the downstream genes.
This study supports a role for negative auto-regulation
in increasing the input dynamic range of downstream
genes. Previous studies suggested this role theoretically
[6,22-24] and demonstrated it using synthetic circuits
[18,21]. Here we tested NAR in the context of a natural
system, the arabinose system of E. coli, in which NAR is
embedded within multiple feedback loops and regulatory
interactions. Disruption of the NAR in the arabinose
system reduced the input dynamic range by an order of
What is the intuitive explanation for the enhancement
of the input dynamic range provided by negative auto-
Figure 5 NAR model suggests increased input dynamic range
for regulators with varying degrees of cooperativity.
Cooperativty of the regulator is described by a Hill coefficient for
the binding of TF to its downstream promoter. Dashed blue line is
the maximal possible input dynamic range that can be reached by
a system with a negatively auto-regulated TF, as found by scanning
the entire range of model parameters. Solid red line is the input
dynamic range that the system displays without negative auto-
regulation, given by R = 81(1/n). The blue square and red dot are the
experimentally measured input dynamic ranges of araBAD with and
without NAR, respectively.
Madar et al. BMC Systems Biology 2011, 5:111
Page 6 of 9
regulation? Negative auto-regulation in the arabinose
system allows the transcription factor concentration to
be modulated by its own input signal. As the concentra-
tion of input signal increases, the concentration of tran-
scription factor also increases. This extends the
response range of downstream promoters, which would
otherwise reach maximal activity when the transcription
factor becomes saturated with input signal.
A related but distinct feature was studied by M. Sava-
geau [6,42], in which proper coupling of inducer levels
and transcription factor levels can increase the output
(as opposed to input) dynamic range of genes: the ratio
of their maximal to minimal expression level.
Use of NAR to increase input dynamic range might be
especially useful for regulators that bind the promoter
cooperatively. Such cooperative binding is thought to
increase specificity . However, a well-known feature
of cooperative binding (high Hill coefficient) is a narrow
input dynamic range . NAR is a simple way to pro-
vide wide input dynamic range, while maintaining coop-
erativity at the level of regulator binding. The
combination of cooperativity and negative auto-regula-
tion might thus provide a response across several dec-
ades of input strength and at the same time remains
The present study adds to our understanding of the func-
tions of negative auto-regulation network motif, showing
that it can increase the input dynamic range of the
response, even when embedded in a relatively compli-
cated native gene circuit. Integration of negative auto-
regulation within a system with high cooperativity (high
specificity and steep activation curve), enables the system
to respond to a wide range of input signal (making the
activation curve wide) while maintaining the system’s
specificity to the signal. This function can be experimen-
tally tested in the numerous additional gene systems
which bear this network motif across organisms. Because
the negative auto-regulation motif is not limited to tran-
scription networks this feature might also apply to other
biological systems including protein-level interactions.
Plasmids (see Table 1)
GFP reporter plasmids (pUA66 based , sc101 ori,
kanR, with gfpmut2 ) for the araC (coordinates
69973-> 70469) and araBAD (coordinates 70469->
69973) promoters are from the fluorescent reporter
library given in detail in . In short: the intergenic
region between araBAD and araC (Figure 1d), with
more than 100 bps of both flanking regions, was incor-
porated twice into the GFP reporter plasmid: once in
the plus strand orientation (araC promoter) and once in
the minus strand orientation (araBAD promoter).
araC was decoupled from its native regulation by
cloning it into the pZE11 plasmid (colE1 ori, ampR,
PLtetO-1) using the KpnI and HindIII restriction
enzymes. The araC gene (the entire coding region) was
PCR amplified from MG1655 genomic DNA with the
following start and end coordinates: 70387-71299 (posi-
tive strand), by using the following primers: 5’ ggcggtac-
catggctgaagcgcaaaatgatcc for the 5’ end and 5’
ggcaagcttccgtcaagccgtcaattgtctg for the 3’ end. The PCR
product and the pZE11 plasmid were digested with
KpnI and HindIII, and then were ligated, yielding
pZE11-araC. A self-ligated pZE11 was generated as well
to serve as a control plasmid.
Table 1 Plasmids and strains used in this study
sc101 ori, promoterless version of the GFP reporter plasmid, (kanR).
GFP reporter plasmid for the araBAD promoter (ParaBADin pUA66), (kanR).
GFP reporter plasmid for the araC promoter, (ParaCin pUA66), (kanR).
Control plasmid: colE1 ori, PLtetO-1promoter, (ampR) .
araC controlled by the tet promoter on pZE11, (ampR).
U423 MG1655z1: MG1655 (F- lambda- ilvG- rfb-50 rph-1) with chromosomal tetR, (specR).
U423 +pZE11 +ParaBADreporter plasmid (specR, ampR, kanR).
U423 with ΔaraC chromosomal deletion (specR).
U425 +pZE11-araC +ParaBADreporter plasmid (specR, ampR, kanR).
U423 +pZE11 +pUA66 (specR, ampR, kanR).
MG1655 (F- lambda- ilvG- rfb-50 rph-1) + ParaCreporter plasmid (kanR).
MG1655 (F- lambda- ilvG- rfb-50 rph-1) + ParaBADreporter plasmid (kanR).
MG1655 (F- lambda- ilvG- rfb-50 rph-1) + pUA66 (kanR).
Madar et al. BMC Systems Biology 2011, 5:111
Page 7 of 9
Strains (see Table 1)
In order to achieve maximal genotypic identity between
the wild-type (with NAR) and the mutant (without
NAR) strains, a modified wild-type strain was con-
structed. tetR gene (z1, specR) from DH5aZ1 was P1
transduced into the wild-type MG1655 chromosome
(K12 strain MG1655: F- lambda- ilvG- rfb-50 rph-1),
yielding strain U423 (MG1655z1). The pZE11 plasmid
and the araBAD reporter plasmid were transformed
into U423, yielding strain U424.
An isogenic ΔaraC strain was obtained by deleting
araC from the MG1655z1 chromosome (coordinates
70391-> 71244) using the phage l Red recombination
system [46,47], yielding strain U425 (MGz1ΔaraC). Pri-
mers 5’ ggacaattggtttcttctctgaatggtgggagtatgaaaagtatggtg-
taggctggagctgcttc 3’ (for the 5 prime end) and 5’
tatcctccttag 3’ (for the 3 prime end) were used to amplify
the kanamycin resistance gene from the pKD4 plasmid
with extensions homologous to the 5’ and 3’ ends of the
araC gene, to allow recombination. Kanamycin resistance
was removed from the deleted strain using FLP recombi-
nase, as described . U425 did not grow on L-Arabi-
nose as a sole carbon source. The araC deletion and the
integrity of the araC\araBAD divergent chromosomal
promoter were verified using PCR and sequencing of the
scar region. araBAD reporter and pZE11-araC plasmids
were transformed into U425, yielding strain U426. Trans-
formation of pZE11-araC into the U425 strain restored
its ability to grow on L-Arabinose as a sole carbon
source. This strain produced AraC levels, similar to that
of the wild-type strain (assessed from the promoter activ-
ity of the araBAD reporter at maximal induction, which
was about 70% of that of the wild-type strain).
MG1655z1 with empty-pZE11 and pUA66 promoter-
less reporter plasmid (strain U427) was used for fluores-
cence background subtraction for U424 & U426.
Strain U66 [35,40] was used for fluorescence back-
ground subtraction for U428.
Growth conditions and measurements
Strains were grown over-night in M9 minimal medium
containing 0.4% glucose 0.05% casamino acids, 50 μg/ml
kanamycin and 100 μg/ml ampicillin (dictated by the
plasmids in each strain) at 37°C. No aTc was used to
induce pZE11-araC, since its basal expression level was
found to be close to the wild type AraC level. Using a
robotic liquid handler (Freedom Evo, Tecan), flat bot-
tom 96-well plates (Nunc) were prepared with 150 μl of
M9 minimal medium containing 0.2% glucose 0.05%
casamino acids, 30 mM cAMP, 50 μg/ml ampicillin and
25 μg/ml. L-Arabinose, in increasing concentrations was
added. The wells were inoculated with the reporter
strain at a 1:600 dilution from the overnight culture.
Wells were then covered with 100 μl of mineral oil
(Sigma) to prevent evaporation (a step which we pre-
viously found not to significantly affect aeration or
growth [48,49], and transferred into an automated incu-
bator. Cells were grown in an incubator with shaking (6
hz) at 30°C for about 20 hr. Every 8 minutes the plate
was transferred by the robotic arm into a multi-well
fluorimeter (Infinite F200, Tecan) that read OD (600
nm) and GFP fluorescence (535 nm).
Promoter activity for each well was calculated from the
OD and GFP measurements after subtracting the OD
and GFP backgrounds. GFP background was obtained
for each well from the promoterless control strains
U427 (for strains U424 and U426) and U66 (for strain
U428) (Additional File 1, Figure S1). Promoter activity
was calculated by computing the rate of accumulation
of GFP per unit time divided by the OD (dGFP/dt/OD)
as described .
Additional file 1: Supplementary on-line material. Detailed model of
the arabinose system and examples for raw data figures.
ampR: ampicillin resistance; cAMP: cyclic adenosine mono phosphate; CRP:
cAMP receptor protein; aTc: anhydrotetracycline; GFP: green fluorescent
protein; kanR: kanamycin resistance; NAR: negative auto-regulation; OD:
optical density; PA: promoter activity; s.e.: standard error; specR:
spectinomycin resistance; TF: transcription factor.
We thank all of our group members for fruitful comments and discussions.
DM thanks R. Milo for his help. This work was supported by the European
Research Council, and the Israel Science Foundation.
DM designed the research, preformed the molecular genetic manipulations
and the experiments, analyzed data and wrote the paper. ED designed the
research, created the mathematical model, analyzed data and wrote the
paper. AB designed the research, preformed the molecular genetic
manipulations, analyzed data and wrote the paper. UA designed the
research, analyzed data and wrote the paper. All authors read and approved
the final manuscript.
The authors declare that they have no competing interests.
Received: 28 April 2011 Accepted: 12 July 2011 Published: 12 July 2011
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Cite this article as: Madar et al.: Negative auto-regulation increases the
input dynamic-range of the arabinose system of Escherichia coli. BMC
Systems Biology 2011 5:111.
Madar et al. BMC Systems Biology 2011, 5:111
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