Identification of Potential Pathway Mediation Targets in
Toll-like Receptor Signaling
Fan Li1, Ines Thiele1,2, Neema Jamshidi1, Bernhard Ø. Palsson1*
1Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America, 2Ph.D. Program in Bioinformatics, University of California
San Diego, La Jolla, California, United States of America
Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-
scale models that incorporate fully functional and biologically relevant features. An extended reconstruction of the human
Toll-like receptor signaling network is presented herein. This reconstruction contains an extensive complement of kinases,
phosphatases, and other associated proteins that mediate the signaling cascade along with a delineation of their associated
chemical reactions. A computational framework based on the methods of large-scale convex analysis was developed and
applied to this network to characterize input–output relationships. The input–output relationships enabled significant
modularization of the network into ten pathways. The analysis identified potential candidates for inhibitory mediation of
TLR signaling with respect to their specificity and potency. Subsequently, we were able to identify eight novel inhibition
targets through constraint-based modeling methods. The results of this study are expected to yield meaningful avenues for
further research in the task of mediating the Toll-like receptor signaling network and its effects.
Citation: Li F, Thiele I, Jamshidi N, Palsson BØ (2009) Identification of Potential Pathway Mediation Targets in Toll-like Receptor Signaling. PLoS Comput Biol 5(2):
Editor: Christopher Rao, University of Illinois at Urbana-Champaign, United States of America
Received May 28, 2008; Accepted January 7, 2009; Published February 20, 2009
Copyright: ? 2009 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by National Institute of General Medical Sciences (grant no. GM68837).
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Toll-like receptors (TLRs) are a group of conserved pattern
recognition receptors that activate the processes of innate and
adaptive immunity . Recent activity has focused on the
characterization of the TLR network and its involvement in the
apoptotic, inflammatory, and innate immune responses [1–3].
TLR signaling is a primary contributor to inflammatory responses
and has been implicated in several diseases including cardiovas-
cular disease [4,5]. Indeed, even in cases of desired inflammatory
response, excessive activation of signaling pathways can lead to
septic shock and other serious conditions .
As such, there is much interest in the development of methods
to attenuate or modulate TLR signaling in a targeted fashion. For
example, one approach involves the inhibition of specific reactions
or components within the TLR network that will dampen
undesired signaling pathways while not adversely affecting other
signaling components [7,8]. These reactions or components should
ideally be highly specific to the TLR network and also to one
transcription target. Therefore, the available, comprehensive data
sets of the TLR network need to be put into a more structured,
systematic format that enables better understanding of the
associated signaling cascades, pathways, and connections to other
cellular networks. Such a systemic approach is necessary to
achieve the ultimate goal of mediating the effects of Toll-like
receptor signaling upon the inflammatory, immune, and apoptotic
responses. This need is particularly important given the amount of
experimental data about TLR signaling that is already too large to
be analyzed by simply viewing the complex web of overlapping
interactions. So far, relatively few attempts have been made to
organize the plethora of experimental data into a single unified
representation . Hence, there is clearly a need to investigate the
function and capabilities of this network using a computational
model, particularly to yield further insights into the mechanistic
action of the TLRs and their immunoadjuvant effects.
Constraint-based reconstruction and analysis (COBRA) meth-
ods represent a systems approach for computational modeling of
biological networks . Briefly, all known biochemical transfor-
mations for a particular system (e.g., metabolic network, signaling
pathway) are collected from various data sources listing genomic,
biochemical, and physiological data [11,12]. The reconstruction is
built on existing knowledge in bottom-up fashion and can be
subsequently converted into a condition-specific model (see below)
[10,13] allowing the investigation of its functional properties
[14,15]. This conversion involves translating the reaction list into a
so-called stoichiometric matrix by extracting the stoichiometric
coefficients of substrates and products from each network reaction
and placing lower and upper bounds (constraints) on the network
reactions. These constraints can include mass-balancing, thermo-
dynamic considerations (e.g., reaction directionality), and reaction
rates (e.g., maximal possible known reaction rate) . Addition-
ally, environmental constraints can be applied to represent
different availabilities of medium components (e.g., various carbon
sources). Many computational analysis tools have been developed
, including Flux balance analysis (FBA). FBA is a formalism in
which a reconstructed network is framed as a linear programming
optimization problem and a specific objective function (e.g.,
growth, by-product secretion) is maximized or minimized .
COBRA methods are well established for metabolic networks and
both reconstruction and analysis tools are widely used .
PLoS Computational Biology | www.ploscompbiol.org1February 2009 | Volume 5 | Issue 2 | e1000292
Furthermore, these methods have been successfully applied to
other important cellular functions such as transcription and
translation , transcriptional regulation , and signaling,
including JAK-STAT  and angiogenesis .
In this study, we present an extended and reformulated model
for the TLR network, reconstructed based on the publicly
available TLR map  and the COBRA approach [11,12].
Signaling networks have been analyzed using extreme pathway
(ExPa) analysis  and FBA . However, since ExPa analysis
becomes computationally challenging in large-scale, mass-balanced
networks , we could not apply this method to the TLR network.
In contrast, network modularization has been established as a
method for reducing large-scale networks into more manageable
units [22–24]. Another approach for reducing network complexity
is to focus on input–output relationships [20,25]. We used FBA to
simplify the mesh of network reactions into ten functionally distinct
input–output (DIOS) pathways, which show different patterns of
signal activation control. Furthermore, we used this modular
representation of the complex TLR signaling network to determine
control points in the network, which are specific for a DIOS
pathway. These control points allow for the modulation of TLR
signaling in a targeted fashion, which will induce a change in
undesired signaling while not having an adverse effect on other
signaling components. Taken together, we show in this study how a
signaling network reconstruction and FBA can be used to identify
potential candidates for drug targeting.
The basis for the network reconstruction was the recently
published Kitano-TLR map, which visualizes the TLR network in
great detail . Since we intended to apply COBRA methods
[14,26], the Kitano-TLR map had to be converted into a self-
consistent, mass- and charge balanced reaction network. Conse-
quently, various modifications and extensions needed to be made
in order to represent the TLR network comprehensively in the
stoichiometric reaction format (Figure 1). These extensions were as
(a) Kinase and phosphatase reactions were added to quantify the
energy (ATP/GTP) consumption by the network reactions.
(b) The addition of ubiquitin ligase components and their
substrate binding allowed for an explicit representation of
the ubiquitination reactions. Additionally, internal transport
reactions were added to enable the transport of network
component between the cellular compartments.
(c) Binding proteins, which induce activation by conformational
change, were added to accurately represent all requisites of a
(d) The Kitano-TLR map represented some reactions in a
manner unsuitable for COBRA modeling purposes by
requiring a number of inputs jointly to activate a certain
output whereas in vivo any single input can trigger the
downstream output. The corresponding reactions were
updated to allow the signal transfer from any ligand to the
These changes were necessary to create a more biologically
relevant model and also to take into account the metabolic and
transport requirements of signaling networks. The resulting model
is able to make predictions in the context of environmental and
energy constraints. Taken together, the conversion to the
stoichiometric representation of the TLR network required
intensive literature search to clarify the status and function of
proteins in the Kitano-TLR map, thereby leading to a
comprehensive curation process. The resulting network was
deemed ihsTLR v1.0, where ‘i’ stands for in silico, ‘hs’ for homo
sapiens and v1.0 is the version number of this in silico TLR
network. The formalism underlying ihsTLR v1.0 is in analogy to
that of metabolic networks and thus enables the usage of COBRA
The use of COBRA methods is heavily dependent on the
configuration of network constraints that model biochemical
properties of the network. For example, in metabolic networks,
enzyme suppression can be modeled by constraining the
appropriate reaction to carry zero net flux. However, in signaling
networks, such as the TLR network, many reactions involve
activation/inactivation of a signaling complex via phosphoryla-
tion. In these cases, the mechanism involves the transfer of a
phosphate group from the kinase to the signaling complex
followed by re-activation of the kinase by the appropriate ATP-
driven reaction (Figure 1B and 1C). When coupled with
dephosphorylation of the signaling complex after signaling, this
mechanism introduces a number of loops into the network
whereby an adequate supply of ATP would seemingly induce
active signaling without actually requiring the presence of a ligand.
Therefore, in order to perform constraint-based analyses on the
TLR network, we manually added constraints on such loop
reactions to require both ATP-fueled phosphorylation as an
energy source and ligand-based signaling input to drive active
signaling (see Materials and Methods).
The ihsTLR v1.0 reconstruction comprised 909 reactions,
which linked 752 distinct chemical species into a self-consistent
network (Table 1). The reconstruction accounted for 14 Toll-like
receptors, 49 ligands, and 6 outputs (see Figure 2 and Tables S1,
S2, S3, S4). A confidence level was assigned to each network
reaction on a scale from one to five, with one being a lack of
conclusive literature evidence and a five being strong, conclusive
literature evidence including review articles (see Materials and
Methods). The average confidence level for the entire network was
3.21, with a total of 306 unique article citations (see Table S7).
Chemical formulae and cellular localization information were also
included. For instance, each species was assigned a chemical
The human innate immune system, as the first line of
defense against pathogens, is a vital component of our
survival. One component of the innate immune system is
the Toll-like receptor signaling network, which is respon-
sible for transmitting activation signals from the outside of
the cell to molecular machinery inside the cell. The innate
immune system must be properly balanced, as excessive
activation can lead to potentially lethal septic shock.
Therefore, there is much interest in developing drugs that
can mediate Toll-like receptor signaling so as to alleviate
effects of excess activation. We present an in silico
reconstruction of the Toll-like receptor signaling network
and convert it into a mathematical framework that is
suitable for constraint-based modeling and analysis. This
approach leads to the identification of potential candi-
dates for drug-based mediation. In addition to identifying
targets for drug mediation of the Toll-like receptor
network, we also supply a network model that may be
continually updated and maintained.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org2February 2009 | Volume 5 | Issue 2 | e1000292
formula for the covalent modification groups (e.g., phosphate,
ubiquitin), which accounted for the metabolic costs of signaling. A
total of six compartments (extra-organism, cytosol, nucleus,
lysosome, endoplasmic reticulum, and vesicle) were considered
to accurately represent the intracellular trafficking. These
additions allowed for a more biologically accurate representation
of the TLR network and for finer control over the network fluxes
through transport and metabolic reactions.
To visualize the network content we created a map of the
ihsTLR v1.0 reconstruction using SimPheny (Genomatica) soft-
ware. All six compartments were represented in the map, with the
appropriate localization for the reactions and components.
Internal transport reactions allowed for the transfer of network
components between the compartments and these reactions were
explicitly positioned on the boundaries of the compartments within
the map. In many cases, reactions that shared substrates or
products were joined on the map to show the interconnections
between the reactions of the TLR network. Overall, an
organization of the map was chosen that enabled the visualization
of the parallel structure of TLR signaling from the extra-cellular
ligand to the transcription-level targets (signal output). The
complete network map can be found in Figure S1.
The topological properties of the ihsTLR v1.0 network were
assessed by determining its node connectivity distribution. The
node distribution defines the degree to which a particular network
component is connected to the entire network, and can be easily
Figure 1. ihsTLR v1.0 reconstruction process. (A) Flowchart illustrating the necessary steps to convert the Kitano-TLR map  into a
stoichiometric, mass-balanced model that can be functionally characterized using COBRA method and FBA. Using these computational tools, it was
possible to determine a set of critical network reactions that are highly-specific candidates for TLR signaling mediation as changes in their activity
attenuate the flux through their corresponding discrete input–output signaling (DIOS) pathways but have no adverse effect on the TLR network
reactions. (B) The transfer of functional groups, such as phosphate groups, is very common in signaling pathways. We accounted for proteins
explicitly in the corresponding network reaction. This created cycles that are artifacts of the modeling and decouple the phosphorylation/
dephosphorylation reactions. Panel B illustrates such a case. The dephosphorylation of PK* to PK can run completely independently of the
phosphorylation reaction of AP to AP* since AP is recovered in a subsequent step. The downstream signaling output is thus not dependent on the
presence of PK*. (C) This panel illustrates how we circumvented this issue during the modeling by creating a sink reaction for AP* and thus
interrupting the cycle formerly present. Since the modeling is only qualitative, the simulation result (e.g., signal yes/no) is not affected by this trick.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org3 February 2009 | Volume 5 | Issue 2 | e1000292
computed. The three most highly connected species were ATP,
ADP, and H+, which participated in 57, 57, and 68 reactions,
respectively (Figure 3). Furthermore, inhibitor of kappa light
polypeptide gene enhancer in B cells kinase (IKK), a non-
metabolic component, was found to participate in 24 reactions,
implying its central role in TLR signaling. Other highly connected
non-metabolic species were a phosphorylated version of IKK and
the MyD88 dimer, which is known to be a key TLR adaptor
protein. Additionally, although both metabolic and non-metabolic
species followed the general power law distribution (Figure 3), it is
notable that most of the more highly connected (participating in
more than 24 reactions) species were metabolites, highlighting the
importance of mass- and charge- balancing of a signaling network
to accurately represent its biological properties.
The normalized connectivity centralization is a commonly used
index of the node connectivity distribution and measures the
extent to which certain nodes are more central than others
independent of actual network size [27,28]. The centralization is
measured from 0 to 1 with a higher value corresponding to the
presence of more highly connected nodes. For the TLR network,
we calculated a centralization index of 0.08, which indicated a
lower level of centralization. For comparison, the centralization
indices of the core metabolic networks of S. pneumoniae and P.
furiosus have been calculated to be 0.24 and 0.10, respectively .
This observation suggests that the network contains fewer nodes
critical for the network functionality and that deletion of these
nodes may disrupt the entire network functionality.
Input–output (I/O) relationships define the set of possible
outputs from a defined set of input cues regardless of the internal
paths connecting the inputs and outputs. As the characterization of
external ligands with their respective TLRs is well established [29–
31], the I/O relationships were considered rather on the level of a
receptor input to a transcription level output. The I/O
relationships were calculated using FBA.
The results of the I/O relationship analysis identified NF-
kappa-B to be the most commonly activated output, as it was
induced by all signaling inputs except for TLR3 (Figure 2). Indeed,
seven of the ten functionally distinct DIOS pathways (discussed
below) resulted in NF-kappa-B activation. The other outputs had
varying degrees of expression, with IRF7 being a single output that
required multiple inputs for activation. Consequently, these in silico
results suggest a relative prevalence of the network to promote NF-
kappa-B activation caused by pathway redundancy.
Furthermore, some ligands can bind to multiple receptors,
which can lead to the activation of an overlapping set of outputs
(Figure 2). For instance, lipopolysaccharide (LPS) binds to TLR2
and TLR4; however, TLR2 activates NF-kappa-B, AP-1, CRE,
and reactive oxygen species (ROS) production, while TLR4
activates the same outputs except for the ROS production, which
is replaced by the IRF3 activation. This redundancy from the
overlapping I/O relationship confers robustness to the network,
since LPS could activate an output, e.g., NF-kappa-B, despite
inhibition of a receptor. An example of this robustness might be
the activation of the NF-kappa-B output in the presence of both
the LPS ligand and a decoy soluble TLR2 receptor. In contrast to
the observed overlapping activation of some outputs, IRF7 was
found to be the only output requiring multiple receptor-ligand
binding for activation (TLR3/4 and TLR7/8/9) (Figure 2). IRF7
has been shown to play a role in the transcriptional activation of
interferon beta chain genes. The reason for the multiple ligand
input is the functional overlap of two activation pathways: complex
formation of MyD88 and IRF7 followed by TRAF6-dependent
phosphorylation of IRF7 and dissociation of the ubiquitinated
TRAF6-MyD88 complex [32–35]. This transactivation induction
mechanism suggests a high level of control for this output.
Distinct I/O Signaling Pathways—DIOS Pathways
We wished to identify candidates for mediation in the TLR
signaling network. To qualify as competent drug targets these
candidates were required to attenuate the TLR signaling in a
targeted fashion, i.e., by inducing changes in the target signaling
pathway while not having an adverse effect on other signaling
components in the TLR network. Subsequently, our calculated I/
O relationships could be used to determine such mediation
candidates as they represented the structure of the complex TLR
signaling network. To further modularize and simplify the
network, we applied FBA to identify sets of signaling reactions
associated with a given input. In a further step, we grouped these
sets of signaling reactions based on their intermediate products. By
doing those, we obtained 10 functionally distinct groups of
signaling reactions, the so-called distinct I/O signaling pathways, or
DIOS pathways. The signaling pathways summarized within one
DIOS pathway thus share the same input, same output, and some,
but not necessarily all, intermediate reactions. In contrast, two
DIOS pathways differ by an input, an output, or an intermediate
reaction (determined by function and experimental evidence). The
10 DIOS pathways triggered signaling from 14 receptors (inputs)
to 6 outputs within the TLR network (Figure 4). Whereas some
signal outputs could be activated by numerous overlapping DIOS
pathways (e.g., NF-kappa-B), other signal outputs (e.g., IRF7)
required multiple receptor-ligand binding events along one single
DIOS pathway for signal mediation. This functional grouping of
network reactions led to a dramatic reduction in complexity by
introducing the DIOS pathways as functional modules of the TLR
Ligand-Linked Reaction Deletion
As defined above, potential signaling mediation targets should
be unique to a DIOS pathway and alteration of the flux through
such targets should affect the performance of the entire DIOS
pathway. Note that such flux alteration will not affect the
remaining TLR signaling network. To identify such control
Table 1. Statistics of ihsTLR v1.0.
Total number of network reactions909
Number of internal network reactions 641
Number of exchange reactions268
Average confidence level3.21
Total number of network species752
Number of ligands49
Number of receptors14
Number of metabolites 23
Number of kinases 158
Number of phosphatases16
Number of outputs6
Total number of discrete signaling pathways 10
The addition of kinases, phosphatases, and binding proteins as well as the
stoichiometric accounting of metabolites, greatly increased the number of
reactions and species from the Kitano-TLR map . This increase in complexity
was necessary to enable the usage of COBRA methods.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org4February 2009 | Volume 5 | Issue 2 | e1000292
Figure 2. The input–output (I/O) relationships of the TLR network at the ligand-receptor-output level. There were a total of 49 ligands (see Table S3 for complete list), 14 receptors, and 6
outputs. Because the ligands are already well characterized with respect to their receptor specificity, it is unnecessary to carry out the input–output analysis at the level of the external ligand. Rather, the
inputs can simply be considered to be signals from the receptors—this reduces the number of inputs from forty-nine to a mere fourteen. NF-kappa-B, CRE/AP-1, and ROS production were all highly
redundant targets as almost all of the receptor inputs activated these outputs. IRF3 and IRF7 were much less redundant and were only activated in the case of a small subset of receptor inputs.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org5 February 2009 | Volume 5 | Issue 2 | e1000292
points, we again employed FBA by determining essential network
reactions along the DIOS pathways. This approach enabled us to
focus on one activation pathway by disregarding alternate
signaling routes that may be activated by a ligand. Therefore, by
limiting the scope of analysis to individual pathways, the
redundancy inherent in the TLR network was bypassed and
control points in the pathway could be readily identified. These
essential network reactions, or control points, are suitable
candidates for TLR signaling mediation. A further subset of the
essential network reactions was determined by requiring the
reactions to have the properties of (i) being specific to a DIOS
pathway, (ii) not affecting the flux through other DIOS pathways,
and (iii) being capable to completely control the flux through a
particular DIOS pathway. This refined subset comprised the so-
called critical network reactions of the TLR network, and
represents the best candidates for TLR signaling mediation.
A total of 41 essential network reactions were found along the
10 DIOS pathways (see Table S6). After applying our specificity
requirements, a subset of eight critical network reactions was
identified to be present in the ihsTLR network along three DIOS
pathways (Figures 5, 6, and 7). These critical network reactions as
well as their known potential as candidates for TLR signaling
mediation are discussed in the following.
Reactive oxygen species (ROS) have been
implicated in a variety of cellular processes including proliferation,
differentiation, and apoptosis . In our analysis of the ihsTLR
network, we identified three critical network reactions in the ROS
production DIOS pathway (Figure 5). Two of these reactions,
Vav1-Rac1/GDP binding and Rac1 phosphorylation, were associated
with the activation of the Rho family GTPase Rac1, which has
been shown to be involved in the production of ROS . The
third critical network reaction, gp91-p22 binding, has been shown to
be necessary for the assembly of NADPH oxidase, which in turn
produces ROS . Deletion of any of these three critical network
reactions reduced the ROS production output through this DIOS
pathway to zero. Additionally, none of the three reactions was
found to have an effect on other DIOS pathways. As such, these
three reactions are strong candidates for mediation of TLR-
induced ROS production.
The various members of the interleukin-1 (IL-1) family
have been implicated in many processes including inflammation,
hematopoiesis, and apoptosis . Various inhibitory factors such
as IL-1R2, soluble IL-1R, and IL-1R antagonist have been
identified and shown to mediate the effects of IL-1 signaling
[40,41]. However, none of these factors can mediate specific IL-1
signaling targets. To this end, we identified three critical network
reactions local to the IL-1 DIOS pathway that could specifically
mediate the IL-1 induced activation of NF-kappa-B (Figure 6).
The three critical network reactions were Ajuba-mediated IRAK1-
PKCz binding, SQST1-PKCz binding, and PKCz phosphorylation (see
Figure 7). Ajuba and SQST1 have been previously shown to
influence IL-1 induced activation of NF-kappa-B [42,43].
Autophosphorylation of the Thr-560 residue on PKCz has also
been independently shown as a prerequisite for enzymatic activity
. Importantly, deletion of any of the three critical network
reactions completely inhibited NF-kappa-B activation via IL-1
signaling without disruption of the IL-1 receptor. Therefore, these
three reactions are suitable candidates for the mediation of IL-1
induced NF-kappa-B activation.
MyD88 has been well characterized as an essential
adaptor protein for TLR signaling, and has been linked with both
NF-kappa-B and AP-1 activation [1,32]. Indeed, MyD88 has been
Figure 3. Node connectivity in ihsTLR v1.0. The rank-ordered results were separated for metabolic and non-metabolic species. The non-
metabolic species include: ligands, receptors, signaling proteins, outputs (see also Table 1). The three most highly connected species were ATP, ADP,
and H+, which participated in 57, 57, and 68 reactions, respectively. In contrast, no non-metabolic species participated in more than 24 reactions. The
node connectivity distribution of metabolic and non-metabolic species followed a power law distribution. The fact that the higher connectivities
were associated with metabolites illustrates the importance of mass- and charge- balanced network reconstructions for biological accuracy.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org6 February 2009 | Volume 5 | Issue 2 | e1000292
shown to associate with all known TLRs except for TLR3, and
thus the MyD88 DIOS pathway is of vital importance to the
overall TLR signaling. Our analysis identified two critical network
reactions in the MyD88 pathway (Figure 7). These two reactions,
MyD88 dimerization and TRAF6/IRAK1 ubiquitination, were both
local to the MyD88 DIOS pathway and did not affect the flux
through other pathways. Moreover, deletion of either of the two
reactions resulted in a complete abrogation of the flux through the
MyD88 pathway, which in turn disrupted the NF-kappa-B and
Taken together, the identification of critical network reactions
in ihsTLR compiled a list of strong candidates for TLR signaling
mediation. Moreover, most of these candidates were non-obvious
targets for signaling mediation as they were not distinguishable
simply by their node connectivities.
In this study, we presented the first large-scale, stoichiometric
reconstruction of the human TLR signaling network, ihsTLR. The
initial reconstruction was based on the Kitano-TLR map  and
manually converted into a format suitable for steady-state
constraint-based modeling by (i) mass- and charge balancing
network reactions, and (ii) adding proteins and energy currency to
the reactions, where appropriate, using TLR-specific literature
(Figure 1 and Table 1). ihsTLR was subsequently converted into a
mathematical model and analyzed with respect to network
connectivity, input–output relationships, and discrete input–
output signaling (DIOS) pathways. A total of 10 DIOS pathways
were identified and 8 critical network reactions were found along
these pathways representing candidates for TLR signaling
mediation. We showed that the combination of signaling network
reconstruction with constraint-based modeling techniques can lead
to highly relevant functional and topological insight into the
network and identification of potential high-specificity drug
The presented network ihsTLR v1.0 is a comprehensive
reconstruction of the TLR signaling pathways and adjacent
signaling pathways. In addition, metabolic cost associated with
signaling was accounted for by including metabolites, such as
ATP, in the network reactions and by creating transport and
exchange reactions for the metabolites. This will enable future
integration of the TLR signaling network with the existing human
metabolic network . Integrated models of metabolism and
signaling have been recently published for small scale networks
[45,46]. Furthermore, the network was analyzed in terms of
pathway activation or inactivation (i.e., ‘on’ versus ‘off’); hence, the
magnitudes of the fluxes through the reactions were not a focus of
the analysis nor were they necessary to determine I/O
relationships. In the future, if data for signaling fluxes through
different pathways become available, they can be directly applied
to the network for more nuanced analysis of pathway activation/
To date few signaling networks have been reconstructed and
modeled using COBRA approaches. Dasika et al.  presented
recently how FBA can be successfully applied to study signaling
Figure 4. An overview of the discrete signaling (DIOS) pathways defined in the TLR network. There were a total of ten pathways that
signaled from input receptor signals to output transcription-level objectives. These ten pathways shared fourteen receptor signals and five output
objectives. The most redundant objective was NF-kappa-B activation, which was the target for a majority of the pathways. Indeed, four of the
pathways—RIP1, NOD1, NOD2, and RIP2/TRIP6/TRAF2—signaled only to NF-kappa-B. However, also note that IL-1 and a large subset of the TLRs
signaled to multiple objectives through a variety of pathways such as PI3K, IL-1, and MyD88. Overall, this receptor-pathway-output format allowed for
a better understanding of the TLR network and its input–output relationships, and also for the calculation of essential reactions as candidates for
signaling mediation. Red: A summary of the eight critical network reactions identified through our analysis (see text). These control points were
located within the ROS production, IL-1, and MyD88 pathways. Although some essential network reactions were identified for the other discrete
signaling pathways, they were unsuitable for selective inhibition due either to their role in other signaling processes or their lack of specificity to a
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org7 February 2009 | Volume 5 | Issue 2 | e1000292
networks. Here, we presented a related method to structure the
complex network content and obtain insight into the functional
network topology. Extreme pathway analysis (ExPa), which was
useful for the topological characterization of the JAK-STAT
network , could not be applied to ihsTLR, as the size of the
network and the connectivity of the species made it infeasible. The
number of ExPas correlates to the size and complexity of the
network , rendering the enumeration of the ExPas computa-
tionally challenging in large networks. The determination of
input–output (I/O) relationships is a great simplification of
complex signaling networks as it treats the network as a ‘‘black
box’’ and asks the simple question of which input instances
activate which output targets [25,47]. Overlapping I/O relation-
ships illustrate a network’s redundancy and robustness within this
black box . In the reconstructed TLR network, we considered
14 distinct input receptors and 6 distinct signaling outputs
(Figure 2). The transcription factor NF-kappa-B was activated by
all but one Toll-like receptor upon ligand binding. This functional
redundancy illustrates the importance of this signaling output for
the entire network, and greatly reflects the involvement of the
TLR signaling network in the inflammatory response, as NF-
kappa-B plays a key role in immune response regulation. In
contrast, the interferon regulatory factor 7 (IRF7) needed the
ligand binding of two independent TLRs for activation, which
Figure 5. A simplified illustration of the reactive oxygen
species (ROS) production DIOS pathway. The three critical
network reactions are highlighted in red. Although there were over
forty reactions in the ROS production pathway, most were associated
with the TLR-induced activation of various phox proteins by the protein
kinases PDK1 and PKCz. However, because PDK1 and PKCz work in
parallel, none of these reactions could control the flux through the
entire pathway, and therefore were not critical network reactions. On
the other hand, the three critical network reactions Vav1-Rac1/GDP
binding, Rac1 phosphorylation, and gp91-p22 binding, produced the two
other components that comprised the final phox protein complex, and
were therefore critical to the overall output ROS production. Note also
that these critical network reactions were localized to the ROS
production pathway and did not interfere with other cellular processes.
Thus, they represent ideal targets for mediation of TLR-induced ROS
Figure 6. A simplified illustration of the IL-1 DIOS pathway.
There were three critical network reactions that controlled the IL-1
induced activation of NF-kappa-B. Uninhibited IL-1 signaling induced
formation of a TRAF6/Ajuba/PKCz/SQST1 complex followed by autop-
hosphorylation at the Thr-560 residue of PKCz. This activated complex
then signaled downstream to NF-kappa-B via IKK phosphorylation. The
three critical network reactions inhibited IL-1 induced NF-kappa-B
activation by preventing the formation and subsequent autopho-
sphorylation of the TRAF6/Ajuba/PKCz/SQST1 complex. Unlike inhibi-
tors such as IL-1R2 and soluble IL-1R, which mediate IL-1 signaling by
preventing the activation of the IL-1 receptors, the three critical
network reactions worked by disrupting other components of the IL-1
DIOS pathway and did not affect the activation of IL-1 receptors.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org8 February 2009 | Volume 5 | Issue 2 | e1000292
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org9 February 2009 | Volume 5 | Issue 2 | e1000292
implies stringent regulation of this factor’s activity. This transcrip-
tion factor IRF7 has been found to be directly involved in immune
response to viral infections by activating IFN-a/b genes .
These I/O relationships, in conjunction with FBA, were used to
identify the specific reactions involved in triggering the signal
between an input and an output. Hereby, input-specific reaction
subsets were identified and grouped based on common interme-
diate products identified through previous experimental valida-
tion. We deemed these reaction sets discrete I/O signaling (DIOS)
pathways, as they spanned the entire signaling network and
modularized the reaction mesh to clearly defined subsets that
could be studied independently (Figure 4). All network reactions
were grouped into 10 DIOS pathways, illustrating again the
redundant, overlapping structure of the TLR network. These
DIOS pathways were then analyzed for essential reactions to
identify control points, or critical network reactions, that allowed
for attenuation of the overall flux through the DIOS pathways.
These critical network reactions controlled the flux through a
DIOS pathway but did not affect the flux through other pathways,
and are therefore ideally suited to the method of selective
inhibition. Selective inhibition of TLR-specific signals involves
mediation of output fluxes without the disruption of components
that are known to play a role in other cellular processes. For
example, the IL-1 inhibitors IL-1R2, soluble IL-1R1, and IL-1R1
antagonist are not suitable for selective inhibition because they
disrupt IL-1 signaling on the whole instead of targeting specific IL-
1 targets. The critical network reactions identified in the IL-1
DIOS pathway can be used for selective inhibition because they do
not disrupt IL-1 signaling, but rather prevent the products of IL-1
signaling from reaching their output objective (in this case, NF-
kappa-B) (Figure 6). Selective inhibition of TLR signaling is
especially important because it is essential for maintaining the
innate immune response and also for enhancing the adaptive
immune response; over-inhibition could lead to a reduction in the
body’s defenses against pathogens, whereas dysfunctional inhibi-
tion can lead to various autoimmune disorders. Therefore, it is
crucial that mediation targets be highly specific.
Our analysis yielded a total of eight critical network reactions
along three of the DIOS pathways (ROS production, IL-1, and
MyD88). A summary of these critical network reactions can be
seen in Figure 4. Although these critical network reactions can
completely mediate the flux through a specific DIOS pathway,
they do not always completely zero out an output. For example,
because NF-kappa-B is a highly activated target, disruption of the
MyD88 pathway does not completely stop NF-kappa-B activation,
as it still occurs via the IL-1 and other pathways (Figure 4). Only in
the absence of other DIOS pathways, the disruption of the MyD88
pathway completely abrogates NF-kappa-B activation. Addition-
ally, the ROS production target is also incompletely inhibited by
disruption of a single DIOS pathway. This robustness appears to
come at a price of specificity as ROS production will be enabled in
multiple environments. However, the two DIOS pathways that
lead to ROS production have different ligand effectors, and are
therefore specific to certain TLRs. The ROS production pathway
has TLR3 as an input, whereas the MyD88 DIOS pathway does
not. Recent studies have shown that TLR3 is vital in host defense
against a variety of infections including West Nile virus , and
this DIOS pathway specificity may hold clues as to why TLR3
plays such an important role. We believe that the inherent
redundancy of the TLR network leads to such crosstalk between
pathways and therefore makes necessary the development of
inhibition combinations that can effectively mediate multiple
One such case may be the atypical protein kinase PKCz, which
is found in two of the reactions (in the ROS production and IL-1
pathways). Removal of the PKCz component from the TLR
network resulted in complete abrogation of both ROS production
and IL-1 induced NF-kappa-B activation, showing that PKCz has
the ability to control multiple objective outputs. In both pathways,
PKCz enzymatic activity is activated by phosphorylation at critical
residues . Disruption of this process would be the physical
equivalent of removing the PKCz component from the TLR
network and could have a powerful inhibitory effect on both ROS
production and IL-1 induced NF-kappa-B activation.
Our constraint-based analysis allowed us to characterize the
aforementioned eight critical network reactions as targets for
selective mediation. Next, we looked to validate these predictions
by searching for experimental evidence of inhibitory roles for
species involved in these critical network reactions. For example,
Vav1, which plays a critical role in the ROS production pathway
in our model, was recently shown to be required as an upstream
signaling protein for NADPH oxidase activity . The role of the
PKCz isozyme in the NF-kappa-B pathway and downstream
cellular functions such as apoptosis has also been heavily studied
[51,52]. Overall, our model is strongly consistent with published
evidence regarding inhibition targets within the TLR signaling
Table 2. Clinical correlates of DIOS pathways.
DIOS Pathway Intermediate Species Related DiseasesReferences
ROS productionRho family GTPases Pro-cancer/neoplastic processes, vascular disease[57,58]
IL-1 IL-1 Rheumatoid arthritis, ankylosing spondylitis, Alzheimer’s disease[59–61]
MyD88 MyD88 Malaria, pneumococcal infections[62–64]
Disruption of the TLR pathways can result in a wide range of pathophysiological conditions. This table summarizes some of the conditions and diseases in which
particular intermediate species are involved, supported by in vivo human and animal studies. The corresponding DIOS pathways are listed in the far left column.
Figure 7. A simplified depiction of the MyD88 DIOS pathway. The two critical network reactions MyD88 dimerization and TRAF6/IRAK1
ubiquitination are highlighted in red. Formation of the MyD88 homodimer favors recruitment of IRAK1 into a complex with TRAF6 . The MyD88
dimer then dissociated from this complex to be either degraded or reused. The second critical network reaction, TRAF6/IRAK1 ubiquitination, occurred
via the ubiquitin-conjugating enzymes Ubc13 and Uev1A, and was necessary for activation of NF-kappa-B and AP-1 through canonical IKK
phosphorylation. Either of the two critical network reactions could completely abrogate the flux through the MyD88 pathway even though the TIR- or
TIRAP-dependent TLR signaling was almost always active.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org10 February 2009 | Volume 5 | Issue 2 | e1000292
network, but also predicts several novel targets that have not been
well studied. Experimental investigation of these critical network
reactions may yield important methods for mediating TLR
signaling and its inflammatory and immune responses. Some
examples of known intermediates involved in diseases are listed in
Table 2. These examples highlight the relevance and importance
of computational characterization of the complex TLR signaling
network to promote further understanding of its role in common
diseases. Additionally, continued curation of the ihsTLR model as
the TLR network is elucidated will allow for further functional
insights into the TLR signaling process. Future additions to this
model may include quantitative fluxes that would allow for the
characterization of relative attenuation quantities and signaling
thresholds. Another avenue of interest would be to study the
dynamics of the TLR network in order to better understand the
temporal nature of the signaling cascade.
Materials and Methods
The TLR network was reconstructed using SimPheny, version
22.214.171.124 (Genomatica), based on a previously described recon-
struction approach [11,12]. An initial framework of reactions and
species was retrieved from a previously published TLR map .
Additional reactions and species were manually added to this
framework with the goal of achieving greater biological relevance
and accuracy. Most of the additions made were taken directly
from literature sources. Some sink and source reactions were
added to eliminate gap conditions and provide for system
boundaries. Chemical formulae were assigned where appropriate:
the generic R group was used for any network compound that was
involved in a mass transfer equation, and all modifications (i.e.,
phosphorylation, ubiquitination, and dimerization) were also
included in the formulae. Additionally, because all of the network
compounds were cellular species, the R group could also be
interpreted as a general fatty acyl chain. Six different compart-
ments were associated with all of the network components and
necessitated the addition of internal transport reactions. These six
compartments were extra-organism, cytosol, nucleus, lysosome,
endoplasmic reticulum, and vesicle. Components that participated
in reactions in multiple compartments were represented by
separate species (e.g., ATP[c], ATP[n], etc.).
Confidence scores were assigned on a scale from zero to five to
every reaction to represent the reliability of the literature sources.
The scale is shown in Table 3.
All of the network reactions were mass- and charge-balanced
and were labeled as either reversible or irreversible. Most of the
transport and all of the exchange reactions were irreversible, and
all of the internal reactions were irreversible on the basis of
corresponding thermodynamic considerations. A list of the
network content can be found in Table S1, S2, S3, S4.
The reconstructed network was represented by a stoichiometric
matrix, S (m6n), where m was the number of network
components (metabolites, proteins, and complexes) and n was
the number of network reactions. Reactions within the network
were mass-balanced such that S?v=0, where v was a steady-state
flux vector [53,54]. Additional constraints on each reaction had
the form ai#vi#bi, where aiand birepresented the lower and
upper limits of the corresponding reaction flux. These additional
constraints were added to reactions that allowed for loops in the
model due either to the recycling of various kinases and
phosphatases or to internal feedback cycles such as those present
in the MAPK pathway (Figure 1B and 1C). These loops occurred
when the activation of a signaling complex was linked with kinase-
driven phosphorylation. Because kinases are recycled for re-use
inside the cell, each kinase-driven phosphorylation reaction was
linked to another reaction that involved the re-activation of the
kinase by either autophosphorylation or some other mechanism as
given in literature (Figure 1B and 1C). When these two reactions
were added to the network, a loop resulted and therefore required
the additional of these manual constraints to prevent false negative
signaling. In non-loop cases, the lower limits aiwere set to zero for
irreversible reactions; whereas biwere used to vary the constraints
on internal network reactions, and to limit the amount of
metabolite available through exchange reactions. For reversible
reactions, aiwas set to -bi. The unit for each reaction flux was
defined to be mmol/gprotein/min. The TLR network model,
including some simulation constraints, can be found in Dataset S1.
The network connectivity was calculated by converting the
stoichiometric matrix S into a binary matrix Sˆsuch that: Sˆij=0, if
Sij=0 and Sˆij=1, if Sij?0. From here, the network connectivity
for each network component xiwas calculated simply by summing
over all j for the row Sˆij.
The distribution of the network connectivity can be represented
by the normalized connectivity centralization, given by
where n is the number of network components and k is a vector of
the node connectivity values. The density is a measure of the mean
off-diagonal adjacency and is given by
These network properties are well established and have been
discussed recently . The normalized connectivity centralization
ranges from zero to one, with a higher value indicative of the
presence of nodes that are far more central than other nodes.
The I/O relationships were calculated using the flux balance
analysis (FBA) encoded in the COBRA toolbox . This analysis
Table 3. Confidence scores for network reactions.
0No literature support. Reaction is added for gap closure.
1 Conflicting/unsubstantiated literature evidence.
2 Some literature support on an assay level—no mechanistic
3Some literature support including mechanistic characterization.
4Strong literature support with repeated results.
5Conclusive literature support.
These scores represent the reliability of the experimental evidence for a given
reaction in the model.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org11February 2009 | Volume 5 | Issue 2 | e1000292
takes as input a single objective reaction and then attempts to
optimize for this objective while maintaining a set of manually
determined constraints. We performed this analysis independently
for six objective reactions—NF-kappa-B phosphorylation, IRF3/ISRE
binding, IRF7/ISRE binding, PKC-induced Phox complex formation, Fos-
Jun-AP1 binding, and CREB-CRE site binding. These six objective
reactions were selected on the basis of their importance as
products of TLR signaling and their role in physiological
symptoms. Baseline flux values were first obtained for each
objective reaction by performing FBA with zero ligand input.
Then, for each objective reaction, we iterated over the set of single
receptor inputs and recorded the objective flux values for each
receptor input with the receptor input flux constrained to
vi=1.0 mmol/gprotein/min. These values were then compared
with the baseline flux through the objective reaction that existed
even without ligand input. Any net positive gain in the flux
through the objective reaction was interpreted to be active
signaling. The input–output relationships of the network were
represented in a matrix format, with each column corresponding
to an objective reaction and each column corresponding to a single
Discrete Input–Output Signaling (DIOS) Pathways
The DIOS pathways were calculated using the same six
objection reactions as in the I/O analysis (see above), and with
the 14 input receptors as defined in Table S3. For each receptor-
output pair, we used FBA to optimize for the output flux. Each vi
for the receptor input was set to be 1.0 mmol/gprotein/min. The
optimized network fluxes were then filtered by removal of loop
reactions (reactions that carried flux even without ligand input).
These loop reactions exist because some enzymes and binding
proteins are recycled after a reaction and therefore are necessary
to accurately represent the TLR network. These loop reactions are
also thermodynamically infeasible without some external balance,
and therefore warrant the application of manual constraints
[55,56]. The effect of these removals is negated by the addition of
sources for any component that may be affected. Thus, control of
the components that participate in loop reactions in essentially
shifted from the loop to a source reaction in our model. For each
loop, we constrained the corresponding enzyme/binding protein
deactivation reaction to vi=0 mmol/gprotein/min. This deleted the
feedback mechanism that would trigger false signaling. A sink
reaction si was added to the model using SimPheny for the
deactivated protein to construct a complete network. From this
modified model, a baseline set of network fluxes was then obtained
by FBA. We then iterated FBA over the set of input–output pairs
without these constraints and subtracted the baseline set to obtain
a reduced set of network fluxes. The reduced set of network fluxes
was then the set of network reactions differentially activated by the
presence of a receptor input. This set was then broken down into
DIOS pathways according to the experimentally verified inter-
mediate components found in a pathway. For example, the
MyD88 DIOS pathway utilizes the MyD88 adaptor protein to
signal downstream. A complete list of these intermediate
components is given in Table S5. Visual inspection of this
differentially activated set was sometimes necessary to distinguish
parallel pathways in which one receptor signaled to the same
output through different DIOS pathways. The process of
identifying DIOS pathways is summarized in the following pseudo
for each receptor input
for each output objective
optimize for maximum objective flux using FBA
remove loop reactions by subtracting baseline reaction set
visual inspection (if necessary) to identify parallel pathways
reduced set of network reactions is a DIOS pathway
This process was carried out using the SimPheny Simulation
module. All transport and metabolite exchange fluxes were
constrained to the arbitrary values of vmin=2500 mmol/gprotein/
min and vmax=500 mmol/gprotein/min. All internal reaction fluxes
were constrained to vmin=0 mmol/gprotein/min and vmax=10 mmol/
gprotein/min.Theobjective functionwasdefinedtomaximize forthe
Critical Network Reactions
Intermediate reactions were selected to represent the flux
through each DIOS pathway. The intermediate reactions selected
were unique to a single DIOS pathway, and accurately
represented signaling through the pathway (see Table S5 for list
of intermediate reactions). Ligand-linked reaction deletion was
then used to analyze each DIOS pathway. Ligand-linked reaction
deletion differed from the typical reaction deletion study in that
only a single DIOS pathway was considered per study, and all
other reactions were constrained to vmin=0 mmol/gprotein/min
and vmax=0 mmol/gprotein/min. All reactions included in the
DIOS pathway were constrained to vmin=0 mmol/gprotein/min
and vmax=10 mmol/gprotein/min, and all transport and metabolite
exchange reactions were constrained to vmin=2500 mmol/
gprotein/min and vmax=500 mmol/gprotein/min. The objective
function was defined to be the intermediate reaction. This new
approach allowed us to bypass the redundancy of the TLR
network and focus on identification of critical network reactions
for each DIOS pathway. For each DIOS pathway, reaction
deletion was performed for each reaction and the flux values of
the intermediate reaction were recorded. These flux values were
then compared with the baseline flux value obtained under
normal conditions. Any reaction that resulted in a complete
impairment of the objective flux value was label to be a critical
All calculations for this study were done using Matlab (Math-
works, Natick, MA) with Tomlab (Tomlab Optimization, Inc,
Pullman, WA) as the linear programming solver.
Found at: doi:10.1371/journal.pcbi.1000292.s001 (0.05 MB ZIP)
TLR_model in Matlab format (zip file)
Found at: doi:10.1371/journal.pcbi.1000292.s002 (3.29 MB PDF)
Map of the reconstructed TLR signaling network
Found at: doi:10.1371/journal.pcbi.1000292.s003 (0.39 MB PDF)
TLR network species
Found at: doi:10.1371/journal.pcbi.1000292.s004 (0.30 MB PDF)
TLR network reactions
Found at: doi:10.1371/journal.pcbi.1000292.s005 (0.02 MB PDF)
TLR network inputs
Found at: doi:10.1371/journal.pcbi.1000292.s006 (0.01 MB PDF)
TLR network outputs
Found at: doi:10.1371/journal.pcbi.1000292.s007 (0.01 MB PDF)
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org12February 2009 | Volume 5 | Issue 2 | e1000292
Found at: doi:10.1371/journal.pcbi.1000292.s008 (0.01 MB PDF)
Critical network reactions
Found at: doi:10.1371/journal.pcbi.1000292.s009 (0.36 MB PDF)
Conceived and designed the experiments: FL IT NJ BØP. Performed the
experiments: FL. Analyzed the data: FL IT NJ. Contributed reagents/
materials/analysis tools: FL. Wrote the paper: FL IT NJ BØP.
1. Kaisho T, Akira S (2006) Toll-like receptor function and signaling. J Allergy Clin
Immunol 117: 979–987; quiz 988.
2. Hoebe K, Jiang Z, Georgel P, Tabeta K, Janssen E, et al. (2006) TLR signaling
pathways: opportunities for activation and blockade in pursuit of therapy. Curr
Pharm Des 12: 4123–4134.
3. Trinchieri G, Sher A (2007) Cooperation of Toll-like receptor signals in innate
immune defence. Nat Rev Immunol 7: 179–190.
4. Mullick AE, Tobias PS, Curtiss LK (2006) Toll-like receptors and atheroscle-
rosis: key contributors in disease and health? Immunol Res 34: 193–209.
5. Stoll LL, Denning GM, Weintraub NL (2006) Endotoxin, TLR4 signaling and
vascular inflammation: potential therapeutic targets in cardiovascular disease.
Curr Pharm Des 12: 4229–4245.
6. Verstak B, Hertzog P, Mansell A (2007) Toll-like receptor signalling and the
clinical benefits that lie within. Inflamm Res 56: 1–10.
7. Liew FY, Xu D, Brint EK, O’Neill LA (2005) Negative regulation of toll-like
receptor-mediated immune responses. Nat Rev Immunol 5: 446–458.
8. O’Neill LA (2006) How Toll-like receptors signal: what we know and what we
don’t know. Curr Opin Immunol 18: 3–9.
9. Oda K, Kitano H (2006) A comprehensive map of the toll-like receptor signaling
network. Mol Syst Biol 2: 2006.0015.
10. Palsson BO (2006) Systems Biology: Properties of Reconstructed Networks. New
York: Cambridge University Press.
11. Reed JL, Famili I, Thiele I, Palsson BO (2006) Towards multidimensional
genome annotation. Nat Rev Genet 7: 130–141.
12. Feist AM, Herrgard MJ, Thiele I, Reed JL, Palsson BO (2009) Reconstruction of
biochemical networks in microorganisms. Nat Rev Microbiol 7: 129–143.
13. Thiele I, Palsson BO (2007) Bringing genomes to life: the use of genome-scale in
silico Models. In: Introduction to Systems Biology. Choi S, ed. Totowa, New
Jersey: Humana Press. pp 14–36.
14. Price ND, Reed JL, Palsson BO (2004) Genome-scale models of microbial cells:
evaluating the consequences of constraints. Nat Rev Microbiol 2: 886–897.
15. Varma A, Palsson BO (1994) Metabolic flux balancing: basic concepts, scientific
and practical use. Nat Biotechnol 12: 994–998.
16. Thiele I, Vo TD, Price ND, Palsson B (2005) An expanded metabolic
reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-
scale characterization of single and double deletion mutants. J Bacteriol 187:
17. Thiele I, Jamshidi N, Fleming RMT, Palsson BO. Genome-scale reconstruction
of E. coli’s transcriptional and translational machinery: a knowledge-base, its
mathematical formulation, and its functional characterization. PLoS Comput
Biol, In press.
18. Gianchandani EP, Papin JA, Price ND, Joyce AR, Palsson BO (2006) Matrix
formalism to describe functional states of transcriptional regulatory systems.
PLoS Comput Biol 2: e101. doi:10.1371/journal.pcbi.0020101.
19. Papin JA, Palsson BO (2004) The JAK-STAT signaling network in the human
B-cell: an extreme signaling pathway analysis. Biophys J 87: 37–46.
20. Dasika MS, Burgard A, Maranas CD (2006) A computational framework for the
topological analysis and targeted disruption of signal transduction networks.
Biophys J 91: 382–398.
21. Yeung M, Thiele I, Palsson BØ (2007) Estimation of the number of extreme
pathways for metabolic networks. BMC Bioinformatics 8: 363.
22. Thiele I, Price ND, Vo TD, Palsson BO (2005) Candidate metabolic network
states in human mitochondria: impact of diabetes, ischemia, and diet. J Biol
Chem 280: 11683–11695.
23. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, et al. (2007) Global
reconstruction of the human metabolic network based on genomic and bibliomic
data. Proc Natl Acad Sci U S A 104: 1777–1782.
24. Yoon J, Si Y, Nolan R, Lee K (2007) Modular decomposition of metabolic
reaction networks based on flux analysis and pathway projection. Bioinformatics
25. Urbanczik R, Wagner C (2005) An improved algorithm for stoichiometric
network analysis: theory and applications. Bioinformatics 21: 1203–1210.
26. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, et al. (2007)
Quantitative prediction of cellular metabolism with constraint-based models: the
COBRA Toolbox. Nat Protoc 2: 727–738.
27. Dong J, Horvath S (2007) Understanding network concepts in modules. BMC
Syst Biol 1: 24.
28. Ma HW, Zeng AP (2003) The connectivity structure, giant strong component
and centrality of metabolic networks. Bioinformatics 19: 1423–1430.
29. Iwasaki A, Medzhitov R (2004) Toll-like receptor control of the adaptive
immune responses. Nat Immunol 5: 987–995.
30. Pasare C, Medzhitov R (2004) Toll-like receptors and acquired immunity.
Semin Immunol 16: 23–26.
31. Takeda K, Kaisho T, Akira S (2003) Toll-like receptors. Annu Rev Immunol 21:
32. Kawai T, Akira S (2006) TLR signaling. Cell Death Differ 13: 816–825.
33. Kawai T, Sato S, Ishii KJ, Coban C, Hemmi H, et al. (2004) Interferon-a
induction through Toll-like receptors involves a direct interaction of IRF7 with
MyD88 and TRAF6. Nat Immunol 5: 1061–1068.
34. Sharma S, tenOever BR, Grandvaux N, Zhou GP, Lin R, et al. (2003)
Triggering the interferon antiviral response through an IKK-related pathway.
Science 300: 1148–1151.
35. Taniguchi T, Ogasawara K, Takaoka A, Tanaka N (2001) IRF family of
transcription factors as regulators of host defense. Annu Rev Immunol 19:
36. Fujisawa T, Takeda K, Ichijo H (2007) ASK family proteins in stress response
and disease. Mol Biotechnol 37: 13–18.
37. Bishop AL, Hall A (2000) Rho GTPases and their effector proteins. Biochem J
38. El-Benna J, Dang PM, Gougerot-Pocidalo MA, Elbim C (2005) Phagocyte
NADPH oxidase: a multicomponent enzyme essential for host defenses. Arch
Immunol Ther Exp (Warsz) 53: 199–206.
39. Dinarello CA (1996) Biologic basis for interleukin-1 in disease. Blood 87:
40. Lang D, Knop J, Wesche H, Raffetseder U, Kurrle R, et al. (1998) The type II
IL-1 receptor interacts with the IL-1 receptor accessory protein: a novel
mechanism of regulation of IL-1 responsiveness. J Immunol 161: 6871–6877.
41. Mantovani A, Locati M, Vecchi A, Sozzani S, Allavena P (2001) Decoy
receptors: a strategy to regulate inflammatory cytokines and chemokines. Trends
Immunol 22: 328–336.
42. Feng Y, Longmore GD (2005) The LIM protein Ajuba influences interleukin-1-
induced NF-kB activation by affecting the assembly and activity of the protein
kinase Cf/p62/TRAF6 signaling complex. Mol Cell Biol 25: 4010–4022.
43. Sanz L, Diaz-Meco MT, Nakano H, Moscat J (2000) The atypical PKC-
interacting protein p62 channels NF-kB activation by the IL-1-TRAF6 pathway.
EMBO J 19: 1576–1586.
44. Standaert ML, Bandyopadhyay G, Kanoh Y, Sajan MP, Farese RV (2001)
Insulin and PIP3activate PKC-f by mechanisms that are both dependent and
independent of phosphorylation of activation loop (T410) and autophosphoryla-
tion (T560) sites. Biochemistry 40: 249–255.
45. Min Lee J, Gianchandani EP, Eddy JA, Papin JA (2008) Dynamic analysis of
integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol 4:
46. Covert MW, Xiao N, Chen TJ, Karr JR (2008) Integrating metabolic,
transcriptional regulatory and signal transduction models in Escherichia coli.
Bioinformatics 24: 2044–2050.
47. Shinar G, Milo R, Martinez MR, Alon U (2007) Input–output robustness in
simple bacterial signaling systems. Proc Natl Acad Sci U S A 104: 19931–19935.
48. Honda K, Yanai H, Negishi H, Asagiri M, Sato M, et al. (2005) IRF-7 is the
master regulator of type-I interferon-dependent immune responses. Nature 434:
49. Daffis S, Samuel MA, Suthar MS, Gale M Jr, Diamond MS (2008) Toll-like
receptor 3 has a protective role against West Nile virus infection. J Virol 82:
50. Roepstorff K, Rasmussen I, Sawada M, Cudre-Maroux C, Salmon P, et al.
(2008) Stimulus-dependent regulation of the phagocyte NADPH oxidase by a
VAV1, Rac1, and PAK1 signaling axis. J Biol Chem 283: 7983–7993.
51. Dempsey EC, Newton AC, Mochly-Rosen D, Fields AP, Reyland ME, et al.
(2000) Protein kinase C isozymes and the regulation of diverse cell responses.
Am J Physiol Lung Cell Mol Physiol 279: L429–L438.
52. Win HY, Acevedo-Duncan M (2008) Atypical protein kinase C phosphorylates
IKKab in transformed non-malignant and malignant prostate cell survival.
Cancer Lett 270: 302–311.
53. Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis.
Curr Opin Biotechnol 14: 491–496.
54. Edwards JS, Ramakrishna R, Schilling CH, Palsson BO (1999) Metabolic flux
balance analysis. In: Metabolic Engineering. Lee SY, Papoutsakis ET, eds. New
York: Marcel Deker.
55. Beard DA, Liang SD, Qian H (2002) Energy balance for analysis of complex
metabolic networks. Biophys J 83: 79–86.
56. Price ND, Thiele I, Palsson BO (2006) Candidate states of Helicobacter pylori’s
genome-scale metabolic network upon application of ‘‘loop law’’ thermody-
namic constraints. Biophys J 90: 3919–3928.
57. Fruehauf JP, Meyskens FL Jr (2007) Reactive oxygen species: a breath of life or
death? Clin Cancer Res 13: 789–794.
58. Papaharalambus CA, Griendling KK (2007) Basic mechanisms of oxidative
stress and reactive oxygen species in cardiovascular injury. Trends Cardiovasc
Med 17: 48–54.
59. Braddock M, Quinn A (2004) Targeting IL-1 in inflammatory disease: new
opportunities for therapeutic intervention. Nat Rev Drug Discov 3: 330–339.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org13 February 2009 | Volume 5 | Issue 2 | e1000292
60. Rojo LE, Fernandez JA, Maccioni AA, Jimenez JM, Maccioni RB (2008) Download full-text
Neuroinflammation: implications for the pathogenesis and molecular diagnosis
of Alzheimer’s disease. Arch Med Res 39: 1–16.
61. Sims AM, Timms AE, Bruges-Armas J, Burgos-Vargas R, Chou CT, et al.
(2008) Prospective meta-analysis of interleukin 1 gene complex polymorphisms
confirms associations with ankylosing spondylitis. Ann Rheum Dis 67:
62. Ghosh S, Hayden MS (2008) New regulators of NF-kB in inflammation. Nat
Rev Immunol 8: 837–848.
63. Ropert C, Franklin BS, Gazzinelli RT (2008) Role of TLRs/MyD88 in host
resistance and pathogenesis during protozoan infection: lessons from malaria.
Semin Immunopathol 30: 41–51.
64. von Bernuth H, Picard C, Jin Z, Pankla R, Xiao H, et al. (2008) Pyogenic
bacterial infections in humans with MyD88 deficiency. Science 321: 691–696.
65. Loiarro M, Capolunghi F, Fanto N, Gallo G, Campo S, et al. (2007) Pivotal
advance: inhibition of MyD88 dimerization and recruitment of IRAK1 and
IRAK4 by a novel peptidomimetic compound. J Leukoc Biol 82: 801–810.
Potential TLR Mediation Targets
PLoS Computational Biology | www.ploscompbiol.org 14February 2009 | Volume 5 | Issue 2 | e1000292