Journal of Cell Science
Models of signalling networks – what cell biologists
can gain from them and give to them
Kevin A. Janes1,* and Douglas A. Lauffenburger2,*
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
2Departments of Biology and Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*Authors for correspondence (firstname.lastname@example.org; email@example.com)
Journal of Cell Science 126, 1913–1921
? 2013. Published by The Company of Biologists Ltd
Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can
simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided
insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about
signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments
but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-
trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own
laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational
model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its
strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.
Key words: Cell signalling, Computational biology, Systems biology
A decade ago, we welcomed the first signalling-network models
that were strongly grounded in wet-lab experiments (Hoffmann
et al., 2002; Schoeberl et al., 2002). Excellent models now exist
for many canonical signalling circuits in a variety of biological
settings. However, such models should not be viewed as an end
product but rather as a tool for addressing systems-level
challenges in cell biology (Janes and Lauffenburger, 2006).
Have models fulfilled this role and have they provided biological
insights that experimentalists should bother to care about? Here,
we answer ‘Yes’ to both questions and predict that signalling-
network models will soon become indispensable for modern
research in the field. Fortunately, the current wealth of data-
intensive methods has primed today’s cell biologists to embrace
modelling, even though they may lack formal training in the
In this Opinion, we propose that empirical cell biologists have
much to gain from signalling-network models, and much to give
by ensuring that these models stay in touch with reality. We
begin with a brief primer on how computational models can be
critically assessed from a biological standpoint. Then, we walk
through a series of important insights about cell signalling that
have stemmed from computational-systems modelling. We
conclude with future perspectives on where signalling-network
models are just beginning to have an impact and will continue to
do so in the coming years.
Evaluating computational models non-
Quantitative models have a rich history in the physical-chemical
sciences and engineering, but such methods are underemphasised
in the life sciences (Bialek and Botstein, 2004). Biology may not
have quite the same theoretical foundations of chemistry or
physics, but that does not necessarily preclude useful modelling.
For example, engineers routinely build models in the face of
unknown variables and incomplete information, using the models
Biologists have much in common with this perspective and
could, thus, find clever ways to incorporate modelling into
studies of signal transduction.
The practical hurdle to modelling for most biologists is the
computational algorithms used for analysis. Although this
fundamental knowledge is good to have, it is not absolutely
required to evaluate a computational model of cell signalling.
Indeed, some of the most influential signalling-network models
have come from established cell-biology labs (Albeck et al.,
2008; Lee et al., 2003; Smith et al., 2002). A good modelling
paper should read no differently than a good experimental paper
– the tools change, but the spirit of the science should remain the
same. There are simply a few key points to keep in mind when
assessing quality and importance.
simplifications. It is said that biologists and theoreticians speak
of two different things when using the term ‘model’ (Di Ventura
et al., 2006; Endy and Brent, 2001). However, both are referring
to useful simplifications of complexity; they just achieve the
simplification in different ways. There are common and
straightforward waysto evaluate
computational model and gauge whether it is providing a
simplification that is biologically meaningful. A fair question
to ask, for example, is whether a simplification is needed at all. A
and the associated
modelsshould be useful
Journal of Cell Science
pathway or network may be so poorly defined or actively
developing that a detailed computational model is premature.
Alternatively, if the core signalling pathway is a processive
enzymatic cascade without feedback loops, it is unlikely that a
computational model of the pathway will reveal anything new
(Huang and Ferrell, 1996). The converse is that simple ‘toy
models’, which explore possible connectivities with arbitrary
parameters, can be extremely useful when cleverly employed
(Box 1). For instance, exhaustive computational searches through
two- and three-protein toy models revealed that only a handful
of signalling configurations can give rise to systems-level
properties, such as perfect adaptation, switch-like behaviour or
cell polarization (Chau et al., 2012; Ma et al., 2009; Shah and
Sarkar, 2011). Computational modelling can thus provide a
useful and efficient way to define the input–output properties of
small signalling circuits (Brandman et al., 2005; Mangan and
Alon, 2003; Tsai et al., 2008).
Another benefit of many computational models lies in their
ability to make predictions. However, biologists should be aware
that not all comparisons between model and experiment are a
priori predictions as they might expect. At the least-stringent
level, models can be ‘tuned’ through their parameters to fit
experimental measurements as closely as possible (Fig. 1A). This
can be useful for training model parameters that are otherwise
difficult to measure; however, the comparison is not a prediction
at all but rather a model calibration. Such calibrations should be
clearly indicated in the figure caption, but one commonly finds
this information missing, which gives the false impression that
the model is making new predictions.
If a training dataset includes multiple experimental conditions,
one can achieve a type of prediction with the training data
through a process called crossvalidation (Fig. 1B). During
crossvalidation, one or more experimental conditions are
withheld from the training set and the model is calibrated with
the remaining data. Then, the calibrated model is challenged to
predict the withheld condition(s), and the withholding-training-
prediction process is iterated through the entire training set.
Crossvalidated predictions are valid, but these can ultimately
yield weak predictions if, for example, the training set is
comprised of seven different doses of the same cytokine
(meaning that the crossvalidation model is calibrated on
information from the other six doses). The most-stringent
predictions are obtained through conditions that are clearly
different than the training set (Fig. 1C), such as perturbations to
the network or timed addition of stimuli (Chatterjee et al., 2010;
Janes et al., 2008; Lee et al., 2012; Miller-Jensen et al., 2007;
Schoeberl et al., 2009). Experimentalists are well-suited to assess
whether data are explicitly in the model training, implied in the
training or outside the training entirely, even if they do not
understand how the training itself is performed.
The last consideration is that there is no ‘one size fits all’
modelling approach, which can universally accommodate the
breadth of applications related to cell signalling. Unlike
technology platforms such as next-generation sequencing, there
is not direct competition among computational methods, and a
single dominant modelling approach will never emerge. The
diversity of models can be daunting because it means that
different mathematics and algorithms are involved for each
application. On the plus side, it focuses the discussion on whether
a modelling approach is best for a specific application rather than
whether it is the best overall (Janes and Lauffenburger, 2006).
This should favour biologists, who are poised to determine
whether the application is compelling and in need of a useful
simplification as described above.
Box 1. From toy models to chemical-kinetic models
The input–output schematics in the Figure illustrate how signalling networks can get complicated very quickly. Even with a simple input–output
system of three proteins (A, B and C), as shown in Figure (i), where the different arrowheads indicate a positive or negative influence, there are
16,038 possible network wirings that connect the input to the output (Ma et al., 2009). Nevertheless, by making a few simplifying assumptions
about each connection and the strength of its influence, one can profile the dynamic properties of all possible 3-protein networks computationally
(top). Pioneering work by Ma and co-workers showed that among all such models, only two general configurations enabled perfect adaptation of
the output to a step-input stimulus (Ma et al., 2009). Similar connectivity constraints have since been uncovered for other systems-level circuit
properties (Chau et al., 2012; Shah and Sarkar, 2011). These computational efforts are conceptually distinct from detailed chemical-kinetic
models, which seek to capture one biological wiring as accurately as possible (ii). Both model types use the same mathematics – ordinary
differential equations (iii) – to capture how information is relayed between proteins.
,, , ...
A + CAC A + C*
d[A]/dt = k-1[AC] – k1[A][C] + kcat[AC]
d[C]/dt = k-1[AC] – k1[A][C]
d[AC]/dt = k1[A][C] – k-1[AC] – kcat[AC]
d[C*]/dt = kcat[AC]
Journal of Cell Science 126 (9) 1914
Journal of Cell Science
Wiring diagrams matter more than the gauge of
An important class of network models involves those that encode
the elementary chemical reactions and transport processes
underlying signal transduction (Aldridge et al., 2006). These
chemical-kinetic models (also called physicochemical models,
comprised of dozens to hundreds of rate equations that describe
how signalling molecules change as a function of others (Box 1).
Each rate equation requires several rate parameters (also called
kinetic constants), which capture how avidly an enzyme acts
upon its substrate, how fast a protein moves from one location to
another in the cell, and how quickly a protein is synthesised or
degraded. Some rate parameters can be gleaned from earlier
biochemical studies that quantified isolated reactions in a test
tube. However, chemical-kinetic models will also have a
substantial number of rate parameters that must be calibrated to
a particular training dataset.
To a cell biologist, this may look like cheating. For example,
you cannot take a microscope image and scale different regions
unevenly to get the picture that you want (Rossner and Yamada,
2004). However, the analogy is flawed, because it assumes that
virtually any picture (i.e. model output) can be achieved with the
free parameters. We now know that this assumption is generally
untrue, as most individual parameters are not leveraged to define
the computed network response (Gutenkunst et al., 2007). Indeed,
one routinely finds that most rate parameters can change over
several orders of magnitude without substantially affecting the
model output (Bentele et al., 2004; Chen et al., 2009; Nakakuki
et al., 2010).
If specific model parameters do not matter so much, then what
does? Over the years, various chemical-kinetic models have
converged upon a common answer: network wiring (Craciun
et al., 2006; Feinberg, 1987; Feinberg, 1988). It turns out that the
particular combination of feedbacks and crosstalk circuits
profoundly influences the behaviour of a signalling network as
a dynamic system (Altan-Bonnet and Germain, 2005; Lander
et al., 2002; Santos et al., 2007). The systems-level functions of
many biologically recurring network configurations have been
studied in detail (Brandman et al., 2005; Chau et al., 2012;
Mangan and Alon, 2003; Tsai et al., 2008). The thematic
importance of wiring is now so recognised that it has been
suggested as a tool for discriminating between competing models
(Harrington et al., 2012; Kuepfer et al., 2007). Models have even
proposed theoretical feedback regulators that have not yet been
identified but must exist in order to reconcile the observed
network dynamics with current knowledge (Nakakuki et al.,
2010). The implication is that we should take great care in
defining the core topology of a signalling network first;
thereafter, we can simply hone in on the handful of rate
processes that exert the greatest leverage on system performance.
Alternatively, there are other modelling formalisms that require
only the topology and a qualitative sense of how proteins and
pathways are logically related (Box 2).
Specific perturbations have complex
Because of the network wirings of biology, there is really no such
thing as a specific perturbation. Signalling networks are so
interconnected that primary targets give rise to secondary effects
and adaptation, which grow to dominate the system over time
(Araujo et al., 2007;Fritsche-Guenther et al.,2011). This isterrible
for biological intuition; for instance, inhibiting Raf or MEKs
should neverleadtohyperactivation ofERKs,butbothdo(Duncan
et al., 2012; Hatzivassiliou et al., 2010; Poulikakos et al., 2010).
modelling have made headway toward deciphering one type of
adaptation: the secondary waves of autocrine signalling triggered
by environmental stimuli. Models of the NF-kB pathway were
used to show that the particular signalling dynamics induced by
bacterial LPS are caused by autocrine synthesis and release of
TNF (Covert et al., 2005; Werner et al., 2005). Modelling the
host-cell response to virus infection also revealed a central role
for NF-kB via autocrine TNF (Garmaroudi et al., 2010),
indicating an innate anti-pathogen response. By statistically
modelling a large TNF-signalling dataset in epithelial cells, we
found that TNF sets off a contingent cascade of multiple
autocrine factors, including TGFa and IL1-family ligands (Janes
et al., 2006). Interestingly, although the individual TNF-induced
interconnectedness and magnitude of autocrine signalling is
highly lineage specific (Cosgrove et al., 2008; Janes et al., 2006).
This suggests a mechanism whereby cell-specific responses to
environmental cues are tuned by the precise signature of
secondary autocrine factors (Amit et al., 2007; Miller-Jensen
et al., 2007; Shvartsman et al., 2002a). The iterative, time-
dependent, and spatial components of autocrine signalling have
provided ample opportunities for network modelling and will
continue to do so (Batsilas et al., 2003; Joslin et al., 2010;
Shvartsman et al., 2002a; Shvartsman et al., 2002b).
Fig. 1. Not all model-measurement comparisons are
created equal. (A) A starting model is first refined through
the process of calibration. Here, model parameters are
trained so that the model matches the calibration data (black
circles) as closely as possible. There are a variety of
parameter-estimation approaches that can be used for model
training at this step. Regardless of the training method,
model estimates of calibration data are not predictions.
(B) Some data can be withheld during calibration (purple
circle) and then reintroduced afterwards to obtain
crossvalidated predictions. (C) Stringent model predictions
relate to new data (red shapes) that are outside of the
original training set.
Signalling network models1915
Journal of Cell Science
Information flow in signalling networks – it is all
In metabolic networks, pathway activity can be gauged directly
by material flux, whereby reactants are converted to products,
which then become the reactants for the next biochemical
conversion (Oberhardt et al., 2009). In signalling networks,
however, the currency of information is not as obvious (Cheong
et al., 2011; Toyoshima et al., 2012). Cells can, theoretically,
perceive the absolute level of a post-translational modification,
its stoichiometry with respect to the unmodified state, the change
relative to baseline or, among other possibilities, the duration that
a modification persists. Properly characterising information flow
is important, as it may help to explain the surprising outcomes
that result when signalling pathways are chronically disrupted by
mutation (Berger et al., 2011; Soussi and Be ´roud, 2001). For
example, inactivation of one copy of the gene encoding the
tumour suppressor PTEN causes early prostate neoplasia, but loss
of both copies drives senescence and suppresses tumorigenesis
(Chen et al., 2005).
Early quantitative experiments suggested that biological
functions were correlated more closely to relative fold changes
in signalling activity than to absolute levels (Miller-Jensen et al.,
2006; Sasagawa et al., 2005). Therefore, cells might rely on a
pathway’s minimum-to-maximum dynamic range to transmit
information (Janes et al., 2008). The fold-change phenomenon
was later studied more formally in a series of concurrent reports,
which found this mode of signal processing in the Wnt–b-catenin
and ERK2 pathways (Cohen-Saidon et al., 2009; Goentoro and
Kirschner, 2009). An accompanying theoretical study also linked
fold-change detection to a particular network wiring called
‘incoherent feed-forward loop’ (IFFL) (Goentoro et al., 2009).
An IFFL is made up of a split pathway, with one branch activating
and the other inhibiting a common downstream effector (Mangan
and Alon, 2003). IFFL-containing network configurations can
achieve perfect adaptation to a step-input stimulus (Box 1) (Ma
et al., 2009). Goentoro and co-workers have shown that the extent
of adaptation is a function of the relative step input rather than the
absolute size of the step (Goentoro et al., 2009). IFFL motifs are
abundant in gene-expression networks and occur in contexts such
as Ras activation, suggesting that fold-change detectionis a widely
recurring theme (Ferrell, 2009; Milo et al., 2002). Collectively,
these studies provide a dramatic simplification for large-scale data
acquisition, because relative quantification of signalling is much
more easily obtained than absolute quantification (Albeck et al.,
2006; Bajikar and Janes, 2012). They also reinforce the added
value of monitoring network responses to perturbations as opposed
to simple measurements of thebaseline signalling state (Irishet al.,
2004; Irish et al., 2006; Janes et al., 2008).
Synergy stops at pairs
Cells regularly receive multiple stimuli at the same time,
providing an opportunity for synergistic signal processing when
the right combinations come together. Mining for synergies
exhaustively seems like an experimentally impractical challenge
at first. To explore all possible combinations of 15 ligands, for
example, would require 215(equalling 32,768) experimental
conditions, more conditions than genes in our genome (Fig. 2).
Number of combinations
8060 40 200
Number of stimuli
Fig. 2. The combinatorial advantage of defining microenvironments
through stimulus pairs. As the number of stimuli increases, the number of
all possible combinations increases exponentially (red; note the logarithmic
scale). By contrast, the number of stimulus pairs increases much less rapidly
(green). For reference, the number of combinations is shown alongside the
estimated number of posttranslational modifications (PTMs), mRNA species,
genes and signalling genes in humans.
Box 2. Formalised network wiring with discrete
As shown in the wiring diagrams of the Figure, biologists often have
a solid qualitative sense of how signalling pathways are configured:
kinase A phosphorylates substrate C, such that when A is active
(‘on’), C isphosphorylated (‘on’), and vice versa(left).Ofcourse,the
biology may be more complicated – two kinases (A, B) could act
upon a substrate redundantly, meaning that A or B gives rise to
phosphorylation of C (middle). Alternatively, substrates may not be
fully engaged unless phosphorylated by two kinases, causing both
A and B to be required (right). These wiring diagrams can be
interconnected and simulated by network models that use discrete
logic to propagate on–off states. Discrete logical models have
recently emerged as tools for cell signalling. Saez-Rodriguez and
co-workers built multiple models of hepatocyte signalling in
response to stimuli and signalling inhibitors (Saez-Rodriguez et al.,
2009; Saez-Rodriguez et al., 2011). Starting with a comprehensive
literature-curated network, the authors refined the wiring to capture
measured patterns of immediate-early signalling. Refinement
involved extensive network ‘pruning’ to remove literature-derived
connections that did not appear to hold true for hepatocytes.
Additionally, their models suggested new links between signalling
proteins that had escaped the curated database but were supported
by the literature or their own follow-on experiments. Discrete logical
models provide a formal mechanism for developing context-specific
signallingnetworksthat aremost consistent with availabledata. The
coarseness of discrete on–off changes in signalling is addressed by
more-complicated ‘fuzzy’ logic models, which allow graded
If B is
If A is
If A is
If B is
If A is
Journal of Cell Science 126 (9)1916
Journal of Cell Science
combined to show that cells operate by a much simpler set of
rules (Janes, 2010).
Synergy or antagonism between pairs of input stimuli is a
recurring theme but one that is rare with respect to all possible
pairwise combinations. Looking at cytokine secretion of
macrophages treated with 22 different ligands, Natarajan and
co-workers detected non-additive interactions in only ,13% of
et al., 2006). Importantly, a number of independent studies have
now shown that quantitative output synergies with higher-order
input combinations are negligible (Chatterjee et al., 2010; Geva-
Zatorsky et al., 2010; Hsueh et al., 2009). This finding is
important, because it opens the door for ‘pairwise scanning’ of
ligands to define cellular response capabilities, followed by linear
or nonlinear modelling to predict the response to more-
complicated inputs (Chatterjee et al., 2010; Geva-Zatorsky
et al., 2010). Doing so allows for a dramatic reduction in the
number of experimental conditions that need to be measured.
In the15-ligand example
the remaining ,32,500 inferred computationally (Fig. 2). Models
of cell signalling can thus improve the efficiency of experimental
designs when used prospectively before a study has started or
while it is underway.
network-level modellingand experimentshave
~231 possible stimulus pairs (Natarajan
~105 conditions would need to be tested, with
Hidden dimensions in complex networks
Combining all of our knowledge about a signalling network yields
a picture that seems irreducibly complex (Caron et al., 2010; Oda
,104proteins (Papin et al., 2005), it may seem remarkable that
anything gets coordinated inside the cell. Nevertheless, several
lines of evidence suggest that there are coherent threads of
simplicity within these networks. For example, despite all the
intricacies of autocrine signalling described earlier, the release of
autocrine EGF-family ligands maps linearly to activation of Ras,
phosphorylation of ERKs and the extent of proliferation (DeWitt
et al., 2001; Joslin et al., 2010). One can search for simple input–
output modules by taking this type of candidate approach, but the
same goal can be achieved faster and more comprehensively by
using statistical ‘data-driven’ models (Janes and Yaffe, 2006).
One common type of data-driven model identifies sets of
measurements that are correlated and groups them together to
identify combinations that accurately predict outputs of interest
(Geladi and Kowalski, 1986). For signal transduction, these
combinations point to ‘hidden dimensions’ within a network,
where multiple signalling proteins may be coordinately regulated to
execute a common function (Jensen and Janes, 2012). Such models
have proved to be remarkably versatile for signalling networks,
capturing adaptors, effectors, cell-fate control and cytokine-release
profiles in different settings (Beyer and MacBeath, 2012; Cosgrove
Janes et al., 2008; Kemp et al., 2007; Kumar et al., 2007a; Kumar
et al., 2007b; Lau et al., 2011; Lee et al., 2012; Miller-Jensen et al.,
2007; Tentner et al., 2012). Therefore, the question is no longer
whether these model-based simplifications of signalling networks
are effective but, rather, why they work so well as often as they do.
Recent theoretical work has suggested that the fundamental
kinetics of cell signalling require only a few hidden dimensions to
obtain a useful approximation, no matter how complicated the
network (Dworkin et al., 2012). These hidden dimensions may
derive from the vigorous degree of crosstalk that interconnects
pathways, enabling a limited spectrum of measurements to contain
2012). For instance, one of our early models suggested a strong link
between the stress kinase MK2 (also known as MAPKAPK2) and
clarified the MK2 mechanism of action through posttranscriptional
stabilization of TNF-induced IL1A, a pro-death autocrine cytokine
that was uncovered after the original model had been built (Janes
et al., 2006; Janes et al., 2008). More recently, the principle of
hidden dimensions has been expanded to interlinked cell responses,
where data-driven modelling helped to reveal a novel necrotic
response to virus infection that occurred together with apoptosis
(Jensen et al., 2013). Statistical models efficiently boil down
complex signal–response datasets to hidden dimensions, which can
then be unpackaged mechanistically with contemporary biological
Clever correlation leads to causality
Modelling can also be used to bend some of the rules ingrained in
signalling research. For example, biologists are commonly
trained that correlation should not be mistaken for causation.
However, that does not mean that correlation-based methods
cannot be used cleverly to uncover new mechanisms (Vilela and
Danuser, 2011). The trick is to observe signalling networks in a
manner that makes spurious correlations unlikely (Fig. 3). Then,
Fig. 3. Separating meaningful correlations from spurious correlations.
(A) When cells are treated with an acute stimulus (black circle), many
pathways are activated concurrently (arrow), and the stimulus dominates the
observed changes. (B) Using sensitive techniques that can discern fluctuations
without an acute stimulus, subtler correlations (assessed by the Pearson
correlation coefficient, R) can be discerned that are hopefully more
meaningful. In the example here, Signal 3 is weakly correlated with the others
(20.2,R,0.2) and signals 4 and 5 are perfectly anti-correlated (R521) in
B, but they all appear correlated (R.0.9) with an acute stimulus in A. This
example exploits the differences in time scales between the slow changes
induced by the stimulus and the fast fluctuations that happen spontaneously.
All plots are shown on the same arbitrary scale.
Signalling network models1917
Journal of Cell Science
simple modelling or analysis can be used to aid hypothesis
generation. In the data-driven approaches described above, for
example, one often measures signalling from a stimulus or
perturbation across a broad landscape of conditions that would
cause spurious correlations to break down.
An alternative method, co-opted from computational signal
processing in engineering, is to look at time-delayed correlations
between nodes within a network. These ‘cross-correlations’ can
point to regulatory interactions, provided that there are no
external driving forces to cause spurious correlations (Dunlop
et al., 2008). For example, if the concentration of one signalling
protein consistently drops shortly after a second protein is
activated, then the resulting cross-correlation will suggest that
the second protein inhibits the first.The cross-correlation would be
weaker with no connection between the two proteins, even if there
were a third protein that controlled both, because two reactions are
harder to coordinate over time than one (Vilela and Danuser,
2011). Cross-correlation-based methods have been used with
speckle microscopy and FRET sensors to unravel the signalling
and mechanical events controlling F-actin assembly and cell
protrusion (Ji et al., 2008; Machacek et al., 2009; Tkachenko et al.,
2011). The analyses were able to clarify the coordination of focal
adhesion assembly, Rho-family GTPase activation, and second-
messenger signalling with a spatial resolution of microns and a
temporal resolution of seconds. As a tool, cross-correlation should
become more widespread with improved reporters that track
multiple signalling events in living cells over time.
Correlations can also be strategically spread out over many
cells instead of many time points. For instance, correlated cell-to-
cell fluctuations in protein expression were recently used to infer
targets of PKA and Tor signalling in yeast (Stewart-Ornstein
et al., 2012). This type of approach does not explicitly require
single-cell measurements, because repeated samplings of small
groups of cells can provide enough fluctuation to organise core
biological functions (Janes et al., 2010). Importantly, a lack of
correlation in this setting can be just as powerful as a positive
or negative correlation. Weaker-than-expected correlations in
FOXO signalling among breast epithelia showed that the FOXO
target-gene network is intersected by RUNX1, another tumour
suppressor that recently has been implicated in breast cancer
(Banerji et al., 2012; Ellis et al., 2012; Janes, 2011; Wang et al.,
2011). Past breakthroughs in biology have stemmed from the
analysis of fluctuations (Luria and Delbru ¨ck, 1943), and so it
would be exciting to see these principles applied more widely
with the molecular tools of today.
The best computational-systems work poses a compelling
biological puzzle that requires a model to solve (Arkin and
Schaffer, 2011). These approaches have already made a positive
impact on how we think about cell biology, but where can models
of signalling go from here? One immediate application lies at the
intersection of signalling networks and targeted therapeutics.
Network-modelling approaches are now being used to identify
new drug targets, drug regimens and mechanisms of drug action
(Kleiman et al., 2011; Lee et al., 2012; Schoeberl et al., 2009).
‘Clean’ molecularly targeted drugs lead to ‘messy’ system-wide
adaptations (see above) (Chandarlapaty et al., 2011; Duncan et al.,
2012; Gioeli et al., 2011), so we expect network models to be
featured more prominently in the future for these purposes.
Longer term, we see potential for models to move ‘downward’
from signalling to gene expression and ‘outward’ from single-cell
to multi-cell behaviour. How transcription-factor binding sites
contribute to gene expression is complicated, but systematic
analyses are beginning to suggest that promoter activity is largely
a function of binding-site location and multiplicity (MacIsaac
et al., 2010; Segal et al., 2008; Sharon et al., 2012). We thus
expect that many new computational models will be developed
that link signalling dynamics to transcriptional signatures (Cheng
et al., 2011; Huang and Fraenkel, 2009). Likewise, as tools
advance for studying single cells at the network level, we
anticipate improved models of cell–cell communication, cell
heterogeneity and multi-cell properties (Anderson et al., 2006;
Feinerman et al., 2008; Jørgensen et al., 2009; Kirouac et al.,
Box 3. An in vivo cell-signalling model evolves through systems-level and hypothesis-driven experiments
A major challenge for in vivo studies of cell signalling lies in properly defining the biological boundaries of the system (grey dashed border in the flow
diagram of the Figure). What are the core and auxiliary cell types involved, and how are they communicating with one another? A recent publication
nicely illustrates how statistical models can interact with directed experiments to redefine in vivo system boundaries over the course of a study (Lau
et al., 2012). The authors sought to examine the contribution of the adaptive immune system to the apoptotic response of intestinal epithelial cells
(IECs) following systemic administrationof tumour necrosisfactor (TNF).Lau and co-workers excluded a rolefor commensal bacteria by showing that
antibiotic treatment had no effect on IEC apoptosis. They then analysed Bioplex assays of cytokines and signalling proteins by using a statistical
modelling approach called partial least squares discriminant analysis (PLSDA), which highlighted a role for monocyte chemotactic protein-1 (MCP1,
also known as CCL2). Surprisingly, later immunohistochemistry(IHC) experiments showed thatMCP1ismost-strongly upregulated and expressed in
goblet cells of the epithelium. Antibody neutralization (Ab neut) of MCP1 accelerated IEC apoptosis, and profiling various lymphoid and myeloid
lineages by FACS showed that MCP1 suppressed the recruitment of plasmacytoid dendritic cells (pDCs). The authors supplemented their PLSDA
model with antibody-neutralization experiments directed at MCP1 and pDCs to uncover a TNF-sensitizing role for interferon-c (IFNc) in their earlier
Bioplex data. The work demonstrateshow modelling can participate in the iterative contraction and expansion of system boundaries that describe cell
signalling in vivo.
Core cell types:
Other cell types:
IECs, T/B cells
IECs, T/B cells
IECs, T/B cells
IECs, T/B cells
IECs, T/B cells
Journal of Cell Science 126 (9) 1918
Journal of Cell Science
2009; Nir et al., 2010). An ambitious but worthwhile long-term
goal should be to build faithful models of cell signalling in vivo,
and work has already begun in this direction (Lau et al., 2012)
(Box 3). Long term, such efforts are likely to require hybrid
modelling approaches that combine different mathematical
formalisms (Anderson et al., 2006; Bajikar and Janes, 2012;
Hayenga et al., 2011). Cell biologists should be able to follow –
and, ideally, participate in – these newer developments with the
same mind-set as outlined above. Remember: the tools may
change, but the thinking is the same.
Computational models of cell signalling were once viewed as
incomprehensible abstractions that were detached from the
biology they claimed to study. Over time, this perception has
changed as computational scientists armed with quantitative
datasets have brought theory to practice. Now is time for
empiricists to meet us in the middle and begin to view modelling
as a legitimate means for studying signal transduction. Network
models are not the answer to every question but just like flow-
cytometry or immunoblotting or quantitative PCR, they should
have their place in every cell biologist’s toolkit.
Work in the laboratory of K.A.J. is supported by the National
Institutes of Health Director’s New Innovator Award Program [grant
number 1-DP2-OD006464], the American Cancer Society [grant
number 120668-RSG-11-047-01-DMC], the Pew Scholars Program
in the Biomedical Sciences, and the David and Lucile Packard
Foundation. This report was partially supported by National
Institutes of Health grants U54-CA112967 (NCI Integrative Cancer
Biology Program), R24-DK090963, and R01-EB010246 to D.A.L.
Deposited in PMC for release after 12 months.
Albeck, J. G., MacBeath, G., White, F. M., Sorger, P. K., Lauffenburger, D. A. and
Gaudet, S. (2006). Collecting and organizing systematic sets of protein data. Nat.
Rev. Mol. Cell Biol. 7, 803-812.
Albeck, J. G., Burke, J. M., Spencer, S. L., Lauffenburger, D. A. and Sorger, P. K.
(2008). Modeling a snap-action, variable-delay switch controlling extrinsic cell death.
PLoS Biol. 6, 2831-2852.
Aldridge, B. B., Burke, J. M., Lauffenburger, D. A. and Sorger, P. K. (2006).
Physicochemical modelling of cell signalling pathways. Nat. Cell Biol. 8, 1195-1203.
Aldridge, B. B., Saez-Rodriguez, J., Muhlich, J. L., Sorger, P. K. and
Lauffenburger, D. A. (2009). Fuzzy logic analysis of kinase pathway crosstalk in
TNF/EGF/insulin-induced signaling. PLOS Comput. Biol. 5, e1000340.
Altan-Bonnet, G. and Germain, R. N. (2005). Modeling T cell antigen discrimination
based on feedback control of digital ERK responses. PLoS Biol. 3, e356.
Amit, I., Citri, A., Shay, T., Lu, Y., Katz, M., Zhang, F., Tarcic, G., Siwak, D.,
Lahad, J., Jacob-Hirsch, J. et al. (2007). A module of negative feedback regulators
defines growth factor signaling. Nat. Genet. 39, 503-512.
Anderson, A. R., Weaver, A. M., Cummings, P. T. and Quaranta, V. (2006). Tumor
morphology and phenotypic evolution driven by selective pressure from the
microenvironment. Cell 127, 905-915.
Araujo, R. P., Liotta, L. A. and Petricoin, E. F. (2007). Proteins, drug targets and the
mechanisms they control: the simple truth about complex networks. Nat. Rev. Drug
Discov. 6, 871-880.
Arkin, A. P. and Schaffer, D. V. (2011). Network news: innovations in 21st century
systems biology. Cell 144, 844-849.
Bajikar, S. S. and Janes, K. A. (2012). Multiscale models of cell signaling. Ann.
Biomed. Eng. 40, 2319-2327.
Banerji, S., Cibulskis, K., Rangel-Escareno, C., Brown, K. K., Carter, S. L.,
Frederick, A. M., Lawrence, M. S., Sivachenko, A. Y., Sougnez, C., Zou, L. et al.
(2012). Sequence analysis of mutations and translocations across breast cancer
subtypes. Nature 486, 405-409.
Batsilas, L., Berezhkovskii, A. M. and Shvartsman, S. Y. (2003). Stochastic model of
autocrine and paracrine signals in cell culture assays. Biophys. J. 85, 3659-3665.
Bentele, M., Lavrik, I., Ulrich, M., Sto ¨sser, S., Heermann, D. W., Kalthoff, H.,
Krammer, P. H. and Eils, R. (2004). Mathematical modeling reveals threshold
mechanism in CD95-induced apoptosis. J. Cell Biol. 166, 839-851.
Berger, A. H., Knudson, A. G. and Pandolfi, P. P. (2011). A continuum model for
tumour suppression. Nature 476, 163-169.
Beyer, E. M. and MacBeath, G. (2012). Cross-talk between receptor tyrosine kinase
and tumor necrosis factor-alpha signaling networks regulates apoptosis but not
proliferation. Mol. Cell Proteomics 11, M111.013292.
Bialek, W. and Botstein, D. (2004). Introductory science and mathematics education for
21st-Century biologists. Science 303, 788-790.
Brandman, O., Ferrell, J. E., Jr, Li, R. and Meyer, T. (2005). Interlinked fast and
slow positive feedback loops drive reliable cell decisions. Science 310, 496-498.
Caron, E., Ghosh, S., Matsuoka, Y., Ashton-Beaucage, D., Therrien, M., Lemieux,
S., Perreault, C., Roux, P. P. and Kitano, H. (2010). A comprehensive map of the
mTOR signaling network. Mol. Syst. Biol. 6, 453.
Chandarlapaty, S., Sawai, A., Scaltriti, M., Rodrik-Outmezguine, V., Grbovic-
Huezo, O., Serra, V., Majumder, P. K., Baselga, J. and Rosen, N. (2011). AKT
inhibition relieves feedback suppression of receptor tyrosine kinase expression and
activity. Cancer Cell 19, 58-71.
Chatterjee, M. S., Purvis, J. E., Brass, L. F. and Diamond, S. L. (2010). Pairwise
agonist scanning predicts cellular signaling responses to combinatorial stimuli. Nat.
Biotechnol. 28, 727-732.
Chau, A. H., Walter, J. M., Gerardin, J., Tang, C. and Lim, W. A. (2012). Designing
synthetic regulatory networks capable of self-organizing cell polarization. Cell 151,
Chen, Z., Trotman, L. C., Shaffer, D., Lin, H. K., Dotan, Z. A., Niki, M., Koutcher,
J. A., Scher, H. I., Ludwig, T., Gerald, W. et al. (2005). Crucial role of p53-
dependent cellular senescence in suppression of Pten-deficient tumorigenesis. Nature
Chen, W. W., Schoeberl, B., Jasper, P. J., Niepel, M., Nielsen, U. B., Lauffenburger,
D. A. and Sorger, P. K. (2009). Input-output behavior of ErbB signaling pathways as
revealed by a mass action model trained against dynamic data. Mol. Syst. Biol. 5, 239.
Cheng, C. S., Feldman, K. E., Lee, J., Verma, S., Huang, D. B., Huynh, K., Chang,
M., Ponomarenko, J. V., Sun, S. C., Benedict, C. A. et al. (2011). The specificity of
innate immune responses is enforced by repression of interferon response elements by
NF-kB p50. Sci. Signal. 4, ra11.
Cheong, R., Rhee, A., Wang, C. J., Nemenman, I. and Levchenko, A. (2011).
Information transduction capacity of noisy biochemical signaling networks. Science
Cohen-Saidon, C., Cohen, A. A., Sigal, A., Liron, Y. and Alon, U. (2009). Dynamics
and variability of ERK2 response to EGF in individual living cells. Mol. Cell 36, 885-
Cosgrove, B. D., Cheng, C., Pritchard, J. R., Stolz, D. B., Lauffenburger, D. A. and
Griffith, L. G. (2008). An inducible autocrine cascade regulates rat hepatocyte
proliferation and apoptosis responses to tumor necrosis factor-alpha. Hepatology 48,
Cosgrove, B. D., Alexopoulos, L. G., Hang, T. C., Hendriks, B. S., Sorger, P. K.,
Griffith, L. G. and Lauffenburger, D. A. (2010). Cytokine-associated drug toxicity
in human hepatocytes is associated with signaling network dysregulation. Mol.
Biosyst. 6, 1195-1206.
Covert, M. W., Leung, T. H., Gaston, J. E. and Baltimore, D. (2005). Achieving
stability of lipopolysaccharide-induced NF-kappaB activation. Science 309, 1854-
Craciun, G., Tang, Y. and Feinberg, M. (2006). Understanding bistability in complex
enzyme-driven reaction networks. Proc. Natl. Acad. Sci. USA 103, 8697-8702.
DeWitt, A. E., Dong, J. Y., Wiley, H. S. and Lauffenburger, D. A. (2001).
Quantitative analysis of the EGF receptor autocrine system reveals cryptic regulation
of cell response by ligand capture. J. Cell Sci. 114, 2301-2313.
Di Ventura, B., Lemerle, C., Michalodimitrakis, K. and Serrano, L. (2006). From in
vivo to in silico biology and back. Nature 443, 527-533.
Duncan, J. S., Whittle, M. C., Nakamura, K., Abell, A. N., Midland, A. A.,
Zawistowski, J. S., Johnson, N. L., Granger, D. A., Jordan, N. V., Darr, D. B. et al.
(2012). Dynamic reprogramming of the kinome in response to targeted MEK
inhibition in triple-negative breast cancer. Cell 149, 307-321.
Dunlop, M. J., Cox, R. S., 3rd, Levine, J. H., Murray, R. M. and Elowitz, M. B.
(2008). Regulatory activity revealed by dynamic correlations in gene expression
noise. Nat. Genet. 40, 1493-1498.
Dworkin, M., Mukherjee, S., Jayaprakash, C. and Das, J. (2012). Dramatic reduction
of dimensionality in large biochemical networks owing to strong pair correlations.
J. R. Soc. Interface 9, 1824-1835.
Ellis, M. J., Ding, L., Shen, D., Luo, J., Suman, V. J., Wallis, J. W., Van Tine, B. A.,
Hoog, J., Goiffon, R. J., Goldstein, T. C. et al. (2012). Whole-genome analysis
informs breast cancer response to aromatase inhibition. Nature 486, 353-360.
Endy, D. and Brent, R. (2001). Modelling cellular behaviour. Nature 409, 391-395.
Feinberg, M. (1987). Chemical-reaction network structure and the stability of complex
isothermal reactors. 1. The deficiency-zero and deficiency-one theorems. Chem. Eng.
Sci. 42, 2229-2268.
Feinberg, M. (1988). Chemical-reaction network structure and the stability of complex
isothermal reactors. 2. Multiple steady-states for networks of deficiency one. Chem.
Eng. Sci. 43, 1-25.
Feinerman, O., Veiga, J., Dorfman, J. R., Germain, R. N. and Altan-Bonnet,
G. (2008). Variability and robustness in T cell activation from regulated heterogeneity
in protein levels. Science 321, 1081-1084.
Ferrell, J. E., Jr (2009). Signaling motifs and Weber’s law. Mol. Cell 36, 724-727.
Fritsche-Guenther, R., Witzel, F., Sieber, A., Herr, R., Schmidt, N., Braun, S.,
Brummer, T., Sers, C. and Blu ¨thgen, N. (2011). Strong negative feedback from Erk
to Raf confers robustness to MAPK signalling. Mol. Syst. Biol. 7, 489.
Signalling network models1919
Journal of Cell Science
Garmaroudi, F. S., Marchant, D., Si, X., Khalili, A., Bashashati, A., Wong, B. W.,
Tabet, A., Ng, R. T., Murphy, K., Luo, H. et al. (2010). Pairwise network
mechanisms in the host signaling response to coxsackievirus B3 infection. Proc. Natl.
Acad. Sci. USA 107, 17053-17058.
Geladi, P. and Kowalski, B. R. (1986). Partial least-squares regression - a tutorial. Anal.
Chim. Acta 185, 1-17.
Geva-Zatorsky, N., Dekel, E., Cohen, A. A., Danon, T., Cohen, L. and Alon,
U. (2010). Protein dynamics in drug combinations: a linear superposition of
individual-drug responses. Cell 140, 643-651.
Gioeli, D., Wunderlich, W., Sebolt-Leopold, J., Bekiranov, S., Wulfkuhle, J. D.,
Petricoin, E. F., 3rd, Conaway, M. and Weber, M. J. (2011). Compensatory
pathways induced by MEK inhibition are effective drug targets for combination
therapy against castration-resistant prostate cancer. Mol. Cancer Ther. 10, 1581-1590.
Goentoro, L. and Kirschner, M. W. (2009). Evidence that fold-change, and not
absolute level, of beta-catenin dictates Wnt signaling. Mol. Cell 36, 872-884.
Goentoro, L., Shoval, O., Kirschner, M. W. and Alon, U. (2009). The incoherent
feedforward loop can provide fold-change detection in gene regulation. Mol. Cell 36,
Gordus, A., Krall, J. A., Beyer, E. M., Kaushansky, A., Wolf-Yadlin, A., Sevecka, M.,
Chang, B. H., Rush, J. and MacBeath, G. (2009). Linear combinations of docking
affinities explain quantitative differences in RTK signaling. Mol. Syst. Biol. 5, 235.
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R. and
Sethna, J. P. (2007). Universally sloppy parameter sensitivities in systems biology
models. PLOS Comput. Biol. 3, 1871-1878.
Harrington, H. A., Ho, K. L., Thorne, T. and Stumpf, M. P. (2012). Parameter-free
model discrimination criterion based on steady-state coplanarity. Proc. Natl. Acad.
Sci. USA 109, 15746-15751.
Hatzivassiliou, G., Song, K., Yen, I., Brandhuber, B. J., Anderson, D. J., Alvarado,
R., Ludlam, M. J., Stokoe, D., Gloor, S. L., Vigers, G. et al. (2010). RAF inhibitors
prime wild-type RAF to activate the MAPK pathway and enhance growth. Nature
Hayenga, H. N., Thorne, B. C., Peirce, S. M. and Humphrey, J. D. (2011). Ensuring
congruency in multiscale modeling: towards linking agent based and continuum
biomechanical models of arterial adaptation. Ann. Biomed. Eng. 39, 2669-2682.
Hoffmann, A., Levchenko, A., Scott, M. L. and Baltimore, D. (2002). The IkappaB-
NF-kappaB signaling module: temporal control and selective gene activation. Science
Hsueh, R. C., Natarajan, M., Fraser, I., Pond, B., Liu, J., Mumby, S., Han, H.,
Jiang, L. I., Simon, M. I., Taussig, R. et al. (2009). Deciphering signaling outcomes
from a system of complex networks. Sci. Signal. 2, ra22.
Huang, C. Y. and Ferrell, J. E., Jr (1996). Ultrasensitivity in the mitogen-activated
protein kinase cascade. Proc. Natl. Acad. Sci. USA 93, 10078-10083.
Huang, S. S. and Fraenkel, E. (2009). Integrating proteomic, transcriptional, and
interactome data reveals hidden components of signaling and regulatory networks.
Sci. Signal. 2, ra40.
Irish, J. M., Hovland, R., Krutzik, P. O., Perez, O. D., Bruserud, O., Gjertsen, B. T.
and Nolan, G. P. (2004). Single cell profiling of potentiated phospho-protein
networks in cancer cells. Cell 118, 217-228.
Irish, J. M., Kotecha, N. and Nolan, G. P. (2006). Mapping normal and cancer cell
signalling networks: towards single-cell proteomics. Nat. Rev. Cancer 6, 146-155.
Janes, K. A. (2010). Paring down signaling complexity. Nat. Biotechnol. 28, 681-682.
Janes, K. A. (2011). RUNX1 and its understudied role in breast cancer. Cell Cycle 10,
Janes, K. A. and Lauffenburger, D. A. (2006). A biological approach to computational
models of proteomic networks. Curr. Opin. Chem. Biol. 10, 73-80.
Janes, K. A. and Yaffe, M. B. (2006). Data-driven modelling of signal-transduction
networks. Nat. Rev. Mol. Cell Biol. 7, 820-828.
Janes, K. A., Albeck, J. G., Gaudet, S., Sorger, P. K., Lauffenburger, D. A. and
Yaffe, M. B. (2005). A systems model of signaling identifies a molecular basis set for
cytokine-induced apoptosis. Science 310, 1646-1653.
Janes, K. A., Gaudet, S., Albeck, J. G., Nielsen, U. B., Lauffenburger, D. A. and
Sorger, P. K. (2006). The response of human epithelial cells to TNF involves an
inducible autocrine cascade. Cell 124, 1225-1239.
Janes, K. A., Reinhardt, H. C. and Yaffe, M. B. (2008). Cytokine-induced signaling
networks prioritize dynamic range over signal strength. Cell 135, 343-354.
single-cell molecular programs by stochastic profiling. Nat. Methods 7, 311-317.
Jensen, K. J. and Janes, K. A. (2012). Modeling the latent dimensions of multivariate
signaling datasets. Phys. Biol. 9, 045004.
Jensen, K. J., Garmaroudi, F. S., Zhang, J., Lin, J., Boroomand, S., Zhang, M., Luo,
Z., Yang, D., Luo, H., McManus, B. M. et al. (2013). An ERK-p38 subnetwork
coordinates host cell apoptosis and necrosis during coxsackievirus B3 infection. Cell
Host Microbe 13, 67-76.
Ji, L., Lim, J. and Danuser, G. (2008). Fluctuations of intracellular forces during cell
protrusion. Nat. Cell Biol. 10, 1393-1400.
Jørgensen, C., Sherman, A., Chen, G. I., Pasculescu, A., Poliakov, A., Hsiung, M.,
Larsen, B., Wilkinson, D. G., Linding, R. and Pawson, T. (2009). Cell-specific
information processing in segregating populations of Eph receptor ephrin-expressing
cells. Science 326, 1502-1509.
Joslin, E. J., Shankaran, H., Opresko, L. K., Bollinger, N., Lauffenburger, D. A. and
Wiley, H. S. (2010). Structure of the EGF receptor transactivation circuit integrates
multiple signals with cell context. Mol. Biosyst. 6, 1293-1306.
Kemp, M. L., Wille, L., Lewis, C. L., Nicholson, L. B. and Lauffenburger, D. A.
(2007). Quantitative network signal combinations downstream of TCR activation can
predict IL-2 production response. J. Immunol. 178, 4984-4992.
Kirouac, D. C., Madlambayan, G. J., Yu, M., Sykes, E. A., Ito, C. and Zandstra, P.
W. (2009). Cell-cell interaction networks regulate blood stem and progenitor cell fate.
Mol. Syst. Biol. 5, 293.
Kirouac, D. C., Saez-Rodriguez, J., Swantek, J., Burke, J. M., Lauffenburger, D. A.
and Sorger, P. K. (2012). Creating and analyzing pathway and protein interaction
compendia for modelling signal transduction networks. BMC Syst. Biol. 6, 29.
Kleiman, L. B., Maiwald, T., Conzelmann, H., Lauffenburger, D. A. and Sorger,
P. K. (2011). Rapid phospho-turnover by receptor tyrosine kinases impacts
downstream signaling and drug binding. Mol. Cell 43, 723-737.
Kuepfer, L., Peter, M., Sauer, U. and Stelling, J. (2007). Ensemble modeling for
analysis of cell signaling dynamics. Nat. Biotechnol. 25, 1001-1006.
Kumar, D., Srikanth, R., Ahlfors, H., Lahesmaa, R. and Rao, K. V. (2007a).
Capturing cell-fate decisions from the molecular signatures of a receptor-dependent
signaling response. Mol. Syst. Biol. 3, 150.
Kumar, N., Wolf-Yadlin, A., White, F. M. and Lauffenburger, D. A. (2007b).
Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine
data. PLOS Comput. Biol. 3, e4.
Lander, A. D., Nie, Q. and Wan, F. Y. (2002). Do morphogen gradients arise by
diffusion? Dev. Cell 2, 785-796.
Lau, K. S., Juchheim, A. M., Cavaliere, K. R., Philips, S. R., Lauffenburger, D. A.
and Haigis, K. M. (2011). In vivo systems analysis identifies spatial and temporal
aspects of the modulation of TNF-a-induced apoptosis and proliferation by MAPKs.
Sci. Signal. 4, ra16.
Lau, K. S., Cortez-Retamozo, V., Philips, S. R., Pittet, M. J., Lauffenburger, D. A.
and Haigis, K. M. (2012). Multi-scale in vivo systems analysis reveals the influence
of immune cells on TNF-a-induced apoptosis in the intestinal epithelium. PLoS Biol.
Lee, E., Salic, A., Kru ¨ger, R., Heinrich, R. and Kirschner, M. W. (2003). The roles of
APC and Axin derived from experimental and theoretical analysis of the Wnt
pathway. PLoS Biol. 1, e10.
Lee, M. J., Ye, A. S., Gardino, A. K., Heijink, A. M., Sorger, P. K., MacBeath, G.
and Yaffe, M. B. (2012). Sequential application of anticancer drugs enhances cell
death by rewiring apoptotic signaling networks. Cell 149, 780-794.
Luria, S. E. and Delbru ¨ck, M. (1943). Mutations of bacteria from virus sensitivity to
virus resistance. Genetics 28, 491-511.
Ma, W., Trusina, A., El-Samad, H., Lim, W. A. and Tang, C. (2009). Defining
network topologies that can achieve biochemical adaptation. Cell 138, 760-773.
Machacek, M., Hodgson, L., Welch, C., Elliott, H., Pertz, O., Nalbant, P., Abell, A.,
Johnson, G. L., Hahn, K. M. and Danuser, G. (2009). Coordination of Rho GTPase
activities during cell protrusion. Nature 461, 99-103.
MacIsaac, K. D., Lo, K. A., Gordon, W., Motola, S., Mazor, T. and Fraenkel, E.
(2010). A quantitative model of transcriptional regulation reveals the influence of
binding location on expression. PLOS Comput. Biol. 6, e1000773.
Mangan, S. and Alon, U. (2003). Structure and function of the feed-forward loop
network motif. Proc. Natl. Acad. Sci. USA 100, 11980-11985.
Miller-Jensen, K., Janes, K. A., Wong, Y. L., Griffith, L. G. and Lauffenburger,
D. A. (2006). Adenoviral vector saturates Akt pro-survival signaling and blocks
insulin-mediated rescue of tumor necrosis-factor-induced apoptosis. J. Cell Sci. 119,
Miller-Jensen, K., Janes, K. A., Brugge, J. S. and Lauffenburger, D. A. (2007).
Common effector processing mediates cell-specific responses to stimuli. Nature 448,
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D. and Alon,
U. (2002). Network motifs: simple building blocks of complex networks. Science 298,
Morris, M. K., Saez-Rodriguez, J., Clarke, D. C., Sorger, P. K. and Lauffenburger,
D. A. (2011). Training signaling pathway maps to biochemical data with constrained
fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli.
PLOS Comput. Biol. 7, e1001099.
Nakakuki, T., Birtwistle, M. R., Saeki, Y., Yumoto, N., Ide, K., Nagashima, T.,
Brusch, L., Ogunnaike, B. A., Okada-Hatakeyama, M. and Kholodenko, B. N.
(2010). Ligand-specific c-Fos expression emerges from the spatiotemporal control of
ErbB network dynamics. Cell 141, 884-896.
Natarajan, M., Lin, K. M., Hsueh, R. C., Sternweis, P. C. and Ranganathan, R.
(2006). A global analysis of cross-talk in a mammalian cellular signalling network.
Nat. Cell Biol. 8, 571-580.
Nir, O., Bakal, C., Perrimon, N. and Berger, B. (2010). Inference of RhoGAP/GTPase
regulation using single-cell morphological data from a combinatorial RNAi screen.
Genome Res. 20, 372-380.
Oberhardt, M. A., Palsson, B. O. and Papin, J. A. (2009). Applications of genome-
scale metabolic reconstructions. Mol. Syst. Biol. 5, 320.
Oda, K. and Kitano, H. (2006). A comprehensive map of the toll-like receptor signaling
network. Mol. Syst. Biol. 2, 2006.0015.
Oda, K., Matsuoka, Y., Funahashi, A. and Kitano, H. (2005). A comprehensive
pathway map of epidermal growth factor receptor signaling. Mol. Syst. Biol. 1,
Papin, J. A., Hunter, T., Palsson, B. O. and Subramaniam, S. (2005). Reconstruction
of cellular signalling networks and analysis of their properties. Nat. Rev. Mol. Cell
Biol. 6, 99-111.
Journal of Cell Science 126 (9) 1920
Journal of Cell Science Download full-text
Poulikakos, P. I., Zhang, C., Bollag, G., Shokat, K. M. and Rosen, N. (2010). RAF
inhibitors transactivate RAF dimers and ERK signalling in cells with wild-type
BRAF. Nature 464, 427-430.
Rossner, M. and Yamada, K. M. (2004). What’s in a picture? The temptation of image
manipulation. J. Cell Biol. 166, 11-15.
Saez-Rodriguez, J., Alexopoulos, L. G., Epperlein, J., Samaga, R., Lauffenburger,
D. A., Klamt, S. and Sorger, P. K. (2009). Discrete logic modelling as a means to
link protein signalling networks with functional analysis of mammalian signal
transduction. Mol. Syst. Biol. 5, 331.
Saez-Rodriguez, J., Alexopoulos, L. G., Zhang, M., Morris, M. K., Lauffenburger,
D. A. and Sorger, P. K. (2011). Comparing signaling networks between normal and
transformed hepatocytes using discrete logical models. Cancer Res. 71, 5400-5411.
Santos, S. D., Verveer, P. J. and Bastiaens, P. I. (2007). Growth factor-induced MAPK
network topology shapes Erk response determining PC-12 cell fate. Nat. Cell Biol. 9,
Sasagawa, S., Ozaki, Y., Fujita, K. and Kuroda, S. (2005). Prediction and validation
of the distinct dynamics of transient and sustained ERK activation. Nat. Cell Biol. 7,
Schoeberl, B., Eichler-Jonsson, C., Gilles, E. D. and Mu ¨ller, G. (2002).
Computational modeling of the dynamics of the MAP kinase cascade activated by
surface and internalized EGF receptors. Nat. Biotechnol. 20, 370-375.
Schoeberl, B., Pace, E. A., Fitzgerald, J. B., Harms, B. D., Xu, L., Nie, L., Linggi, B.,
Kalra, A., Paragas, V., Bukhalid, R. et al. (2009). Therapeutically targeting ErbB3:
a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci. Signal. 2,
Segal, E., Raveh-Sadka, T., Schroeder, M., Unnerstall, U. and Gaul, U. (2008).
Predicting expression patterns from regulatory sequence in Drosophila segmentation.
Nature 451, 535-540.
Shah, N. A. and Sarkar, C. A. (2011). Robust network topologies for generating
switch-like cellular responses. PLOS Comput. Biol. 7, e1002085.
Sharon, E., Kalma, Y., Sharp, A., Raveh-Sadka, T., Levo, M., Zeevi, D., Keren, L.,
Yakhini, Z., Weinberger, A. and Segal, E. (2012). Inferring gene regulatory logic
from high-throughput measurements of thousands of systematically designed
promoters. Nat. Biotechnol. 30, 521-530.
Shvartsman, S. Y., Hagan, M. P., Yacoub, A., Dent, P., Wiley, H. S. and
Lauffenburger, D. A. (2002a). Autocrine loops with positive feedback enable
context-dependent cell signaling. Am. J. Physiol. Cell Physiol. 282, C545-C559.
Shvartsman, S. Y., Muratov, C. B. and Lauffenburger, D. A. (2002b). Modeling and
computational analysis of EGF receptor-mediated cell communication in Drosophila
oogenesis. Development 129, 2577-2589.
Smith, A. E., Slepchenko, B. M., Schaff, J. C., Loew, L. M. and Macara, I. G. (2002).
Systems analysis of Ran transport. Science 295, 488-491.
Soussi, T. and Be ´roud, C. (2001). Assessing TP53 status in human tumours to evaluate
clinical outcome. Nat. Rev. Cancer 1, 233-240.
Stewart-Ornstein, J., Weissman, J. S. and El-Samad, H. (2012). Cellular noise
regulons underlie fluctuations in Saccharomyces cerevisiae. Mol. Cell 45, 483-493.
Tentner, A. R., Lee, M. J., Ostheimer, G. J., Samson, L. D., Lauffenburger, D. A.
and Yaffe, M. B. (2012). Combined experimental and computational analysis of
DNA damage signaling reveals context-dependent roles for Erk in apoptosis and G1/S
arrest after genotoxic stress. Mol. Syst. Biol. 8, 568.
Tkachenko, E., Sabouri-Ghomi, M., Pertz, O., Kim, C., Gutierrez, E., Machacek,
M., Groisman, A., Danuser, G. and Ginsberg, M. H. (2011). Protein kinase A
governs a RhoA-RhoGDI protrusion-retraction pacemaker in migrating cells. Nat.
Cell Biol. 13, 660-667.
Toyoshima, Y., Kakuda, H., Fujita, K. A., Uda, S. and Kuroda, S. (2012). Sensitivity
control through attenuation of signal transfer efficiency by negative regulation of
cellular signalling. Nat. Commun 3, 743.
Tsai, T. Y., Choi, Y. S., Ma, W., Pomerening, J. R., Tang, C. and Ferrell, J. E., Jr
(2008). Robust, tunable biological oscillations from interlinked positive and negative
feedback loops. Science 321, 126-129.
Vilela, M. and Danuser, G. (2011). What’s wrong with correlative experiments? Nat.
Cell Biol. 13, 1011.
Wang, L., Brugge, J. S. and Janes, K. A. (2011). Intersection of FOXO- and RUNX1-
mediated gene expression programs in single breast epithelial cells during
morphogenesis and tumor progression. Proc. Natl. Acad. Sci. USA 108, E803-E812.
Werner, S. L., Barken, D. and Hoffmann, A. (2005). Stimulus specificity of gene
expression programs determined by temporal control of IKK activity. Science 309,
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