Vol. 28 no. 5 2012, pages 694–700
Modeling community-wide molecular networks of multicellular
Divisions of Experimental Hematology and Cancer Biology, Human Genetics and Biomedical Informatics, Cincinnati
Children’s Hospital Medical Center, Cincinnati, OH, USA
Associate Editor: Trey Ideker
Advance Access publication December 30, 2011
Motivation: Multicellular systems, such as tissues, are composed
of different cell types that form a heterogeneous community.
Behavior of these systems is determined by complex regulatory
networks within (intracellular networks) and between (intercellular
networks) cells. Increasingly more studies are applying genome-
wide experimental approaches to delineate the contributions of
individual cell types (e.g. stromal, epithelial, vascular cells) to
collective behavior of heterogeneous cell communities (e.g. tumors).
Although many computational methods have been developed for
analyses of intracellular networks based on genome-scale data,
these efforts have not been extended toward analyzing genomic data
from heterogeneous cell communities.
Results: Here, we propose a network-based approach for analyses
of genome-scale data from multiple cell types to extract community-
wide molecular networks comprised of intra- and intercellular
interactions. Intercellular interactions in this model can be physical
interactions between proteins or indirect interactions mediated by
secreted metabolites of neighboring cells. Applying this method on
data from a recent study on xenograft mouse models of human
lung adenocarcinoma, we uncover an extensive network of intra-
and intercellular interactions involved in the acquired resistance to
Supplementary information: Supplementary data are available at
Received on September 15, 2011; revised on November 10, 2011;
accepted on December 25, 2011
Individual cells within a heterogeneous cell population interact with
each other through secreted molecules and membrane proteins,
sometimes referred to as cross-talk (Frankenstein et al., 2006;
Jahoda and Christiano, 2011; Kiel and Morrison, 2008). At the
molecular level, this population can be viewed as a community-
wide network of molecular interactions comprising intracellular
interactions within each cell as well as intercellular interactions
of molecules of different cells. Since population characteristics
as a whole are highly dependent on the intra- as well as
intercellular networks, the global architecture of the community-
wide molecular network (CMN, made of intra- and intercellular
∗To whom correspondence should be addressed.
molecular interactions) can determine the collective behavior of
heterogeneous cell communities.
Network-based analyses of genomic data have shed light on the
global organization of intracellular networks contributing to normal
and malignant behavior of cells (Basso et al., 2005; Calvano et al.,
2005; Chuang et al., 2007;Tomlins et al., 2007). Mounting evidence
now suggests that interplays of cells within a microenvironment
can give rise to complex population behavior (Frankenstein et al.,
2006; Jahoda and Christiano, 2011). Such complex interactions of
cells within a population have been observed in developmental
processes (Kirouac et al., 2010; Lai, 2004), in stem cell niches
microenvironments (Boersma et al., 2008; Coussens and Werb,
2002). However, most of the studies on deciphering the complex
pattern of molecular network interactions in such multicellular
systems and their role in population-wide collective behavior have
been focused on a limited number of molecules. Although some
notable large-scale studies in some experimental systems have
been undertaken (Frankenstein et al., 2006; Kirouac et al., 2010),
the computational methodology of analysis primarily involved
candidate-based approaches, limiting the scope of analysis. More
powerful computational methods for analyses of genomic data from
heterogeneous cell populations would therefore greatly enhance our
ability to gain insight into the organization of CMNs and their role
in the collective population behavior.
Here, in order to enable modeling of molecular networks
of whole-cell populations, we developed a network model of
community-wide molecular interactions by combining intracellular
interactions from each cell type and their intercellular connections
into a single global network (community molecular network)
(Fig. 1). We use this global network in conjunction with the
genome-wide gene expression data from different cell types to
extract networks of interest showing community-wide molecular
interactions most highlighted by the data. We integrate genomic
data with the global network using NetWalk (Komurov et al.,
2010), a computational algorithm for seamless integration of
genomic data with molecular networks. The advantage of NetWalk
compared to other network analysis tools is that NetWalk takes
into account the whole-data distribution without requiring statistical
cutoffs or predetermined gene lists of interest. NetWalk output
is a distribution of Edge Flux (EF) values containing numeric
score of relevance assigned to each interaction in the network.
EF values, just like in gene expression data, can be used for
further statistical analyses, allowing for direct network-based
statistical analyses. Using this platform, we present an analysis
of recently published gene expression data from epithelial and
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Molecular networks of multicellular systems
Fig. 1. CMN model. (A)An imaginary network of intracellular interactions.
The circle represents plasma membrane. Four nodes have been numbered
to demonstrate intercellular interaction types. Nodes 1 and 2 represent
extracellular proteins, and 3 and 4 are enzymes performing consecutive
metabolic reactions (product of the reaction catalyzed by 3, C, is a reactant
of that catalyzed by 4). (B) CMN of two different cell types. Since proteins
1 and 2 are extracellular, protein 1 in one cell type can interact with protein
2 in the other, and vice versa. In addition, since the metabolite C is secreted
into the extracellular space, the reaction performed by enzyme 3 in one cell
can affect the reaction performed by enzyme 4 in the other.
stromal cells of a mouse xenograft model of acquired resistance
to bevacizumab (angiogenesis inhibitor) (Cascone et al., 2011), and
identify previously unrecognized CMNs of stromal and epithelial
cells involved in acquired resistance to this targeted therapy.
First, we compiled a comprehensive network of biomolecular interactions
between human genes. In order to account for physical and indirect
functional interactions, we compiled (i) protein–protein interactions; (ii)
transcription factor–target pairs; (iii) interactions based on neighboring
metabolic reactions; and (iv) neighboring functional interactions. Protein–
protein interactions were obtained from HPRD [Human Protein Reference
Database, Mishra et al. (2006)], BIND [Biomolecular Interaction Database,
Bader et al. (2001)], MINT (Chatr-aryamontri et al., 2007), BioGRID
(Stark et al., 2006) and IntAct (Kerrien et al., 2007). Directed signaling
interactions were obtained from Kyoto Encyclopedia of Genes and Genomes
(KEGG) (Kanehisa and Goto, 2000) and National Cancer Institute (NCI)
Pathway Interaction Database (http://pid.nci.nih.gov/). Interactions from
MINT, BioGRID, IntAct and NCI were obtained from Pathway Commons
(Cerami et al., 2011). Transcription factor–target interactions were obtained
from BIND (queried as protein–DNA interactions), Reactome (Joshi-Tope
et al., 2005) (obtained from Pathway Commons), TRANSFAC (Wingender
et al., 2000) and NCI Pathway Interaction Database (obtained from Pathway
Commons). Neighboring metabolic reactions are assigned to a pair of genes
if the product of the reaction catalyzed by one gene product is the reactant
of the reaction catalyzed by the other. As such, metabolic interactions are
directed (A → B, product of reaction catalyzed by A is a reactant for B).
Global network of molecular relationships
For example, hexokinase II (HK2) catalyzes the reaction glucose + ATP
→ glucose-6-phosphate + ADP, and glucose phosphate isomerase (GPI)
catalyzes the reaction glucose-6-phosphate → fructose-6-phosphate. Since
these two genes are assigned an interaction HK2 → GPI in the network. See
genes and their metabolic reactions was obtained from KEGG. Neighboring
functional interactions were obtained from Reactome.Altogether, the global
network consists of 13 256 unique genes connected by 170 735 interactions.
The global network of biomolecular relationships described above accounts
for intracellular interactions between genes. This network is defined as aij,
which is 1 if genes i and j interact, 0 otherwise. In case of multiple cell
types within the same community, the intracellular networks for each cell
are defined separately, akij, where k is the cell type k. We define the CMN
(of two cell types, for simplicity) as
for each cell type, and r12 is the intercellular adjacency matrix between
molecules of cell types 1 and 2. The matrix r12ijis 1, only if the gene i in
cell type 1 interacts with gene j in cell type 2, and 0 otherwise. Gene i in cell
type 1 interacts with gene j in cell type 2 if one of the following is true:
1. The interaction ij is a protein–protein interaction or Reactome
neighboring interaction and gene i encodes an extracellular secreted
protein and gene j encodes an extracellular secreted protein or a
Community molecular network model
2. Theinteractionij isaneighboringmetabolicreactionandthecommon
metabolites in the respective reactions catalyzed by gene i and j is a
These two rules ensure that only those gene pairs capable of functionally
interacting at the intercellular level are assigned an interaction. The first rule
is for physical interactions that are possible only if the two proteins can be
in a physical proximity of each other (type 1 intercellular interactions). The
reaction performed by one can affect the reaction performed by the other,
which can only happen if the common metabolite of the two reactions is
secreted [i.e. subcellular location of the metabolite is defined in the Human
Metabolome Database (Wishart et al., 2009) as ‘extracellular’] (type 2).
Although analyses of cell–cell interactions within a cell population have
primarily focused on physical interactions defined by type 1 interactions
above, metabolic interactions between enzymes of different cells can also be
of high importance in defining population behavior. One such mechanism
exists in normal brain (Dienel and Cruz, 2003) and in some tumors
(Pavlides et al., 2009) for shuttling of lactate from lactate-producing cells
to lactate-oxidizing cells, thereby forming a metabolic symbiosis within the
multicellular system. A recent study of large-scale modeling of metabolic
interactions between cell types in the human brain is also an excellent
example underlining the importance of this type of interactions (Lewis et al.,
Overall, the network defined by A is a single molecular network of the
whole-cell community (CMN) that contains information on intra- as well as
intercellular network interactions (Fig. 1).As expected, most of the CMN is
composed of intracellular interactions, with intercellular interactions making
up only slightly >6% of all interactions in the CMN.Approximately 20% of
intercellular interactions in our model are composed of type 2 interactions
Gene expression data used in the analyses were obtained from Cascone
et al. (2011) (NCBI GEO: GSE26644). Briefly, t-tests were performed
Data integration and NetWalk algorithm
on each gene to score significance of expression change between
tumor or stromal cells before and after bevacizumab resistance. This
generated a t-value of significance for each gene in tumor and stromal
cells, respectively. Before analyses, mouse genes in stromal dataset
were converted to corresponding human orthologous genes by using
the mapping provided at the Mouse Genome Informatics Database
We utilize a data integration approach that takes into account the whole
distribution of the data without relying on manual statistical cutoffs. Such
seamless integration of the data with the network is done using the
data-biased random walk algorithm, NetWalk. Briefly, NetWalk integrates
genomic data with networks of interactions between genes to score the
relevance of each interaction based on both the data values of the genes
as well as their local network connectivity. This results in a distribution
of EF values that can be used for dynamic reconstruction of user defined
networks. EF values can be further subjected to statistical analyses such
as clustering, allowing for direct numerical comparisons of context-specific
networks between different conditions.
First, the experimental data are integrated with the network to form a
transition probability matrix for random walk
where pij is the transition probability from node i to node j, wj is the
experimental value for node j, and Niis the set of immediate downstream
neighbors (undirected edges are considered bidirectional) of node i.The final
relevance score (g) for each node is calculated by
where q is the restart probability (we use q=0.01). EF values are
where eijis the flux through edge ij and represents the score of importance
of the interaction based on the data. In order to control for topological bias
in the network, we also calculate
where eris the edge score distribution vector calculated by letting wi=1 for
all i. Finally, the NetWalk output is the distribution of normalized EF values
This gives the final normalized score distribution of edges, which reflects
EFs of nodes relative to what would be expected by topology alone in
the given network. The EF distribution contains relevance scores assigned
to interactions within each cell type as well as to interactions between
cells. The EF distribution can be queried for networks most highlighted
by the data within the whole-cell community (intracellular and intercellular
interactions combined), within individual cell types or only for intercellular
NetWalk is available in a software suite NetWalker (Komurov
et al., submitted for publication, http://research.cchmc.org/netwalker).
Implementation and inclusion of the CMN model into NetWalker are
currently in progress. However, an R file-containing interaction list objects
for our comprehensive network and a pre-constructed CMN for two cell
types can be found in the Supplementary Material.
As a proof of concept analysis using our CMN model, we chose to
use the data from a recent study utilizing mouse xenograft models
to study acquired resistance of human lung adenocarcinomas to
angiogenesis inhibitors (Cascone et al., 2011). The authors in the
aforementioned study injected H1975 cells (human non-small cell
lung cancer cell line that is sensitive to bevacizumab) into mice
and after a while started treating them with a vehicle control or
resistance to the drug. Gene expression microarray analyses were
then performed on control and bevacizumab-resistant tumors (n=3
for each) using Illumina mouse-specific (WG-6 v2) and human-
specific (WG-6 v3) arrays. Mouse-specific arrays were used to
measure stromal gene expression and the human array was used
to measure the tumor gene expression.
in the acquired resistance to bevacizumab, we first performed t-tests
on each gene in the stromal and tumor datasets to score the extent
of their change in resistant versus control cases. The resultant
t-values (the value of t-statistic from t-tests) for tumor and stromal
gene expression data had different distributions; stromal cells had a
wider t-value distribution (not shown), consistent with the original
study’s report that stromal gene expression changes were more
pronounced than that in tumor data. Therefore, we transformed
the two t-value distributions using quantile normalization to obtain
identical distributions for tumor and stromal t-values. These
t-values (two sets, one for stromal and one for tumor cells) were
used as node weights for our NetWalk-based network analyses
Fig. 2. Workflow of the method in this study. First, t-tests were performed
for each gene to score the difference of their expression between control and
bevacizumab-resistant samples in tumor and stroma datasets, respectively.
CMN was created for two cell types, and the resultant t-values were overlaid
Note that the EF distribution contains scores for intracellular interactions in
each cell type as well as intercellular interactions.
Molecular networks of multicellular systems
Fig. 3. NetWalk analysis of CMN. (A) Fraction of intracellular interactions in cells 1 and 2 (i.e. tumor and stroma) and intercellular interactions within the
indicated number of highest scoring EF values from the CMN of two cell types. (B) Boxplot of t-value distributions of genes for cells 1 and 2 within the
networks generated from indicated number of highest scoring EF values. Network of highest-scoring EF values for the (C) tumor and (D) stromal t-values.
Nodes are colored by their t-values according to the color key. Interactions are colored by their type as indicated. Some notable subnetworks have been
highlighted by boxes.
First, we constructed a CMN model of two cell types: tumor (T) and
stroma (S). This CMN is defined as
NetWalk analysis of CMN
where Atumor:stroma is the adjacency matrix of this CMN, T is
the adjacency matrix for tumor and S is the adjacency matrix for
stromal intracellular networks (note that T and S are identical,
we denoted them separately for clarity), rt:s is the adjacency
matrix for intercellular interactions between tumor and stromal cells
defined by rules in ‘Methods’ section. This model is composed
of 26512 genes (13256 genes/cell type) connected by 375570
interactions (all intra- and intercellular interactions). By overlaying
both t-value distributions onto the respective nodes on this CMN,
we performed NetWalk analysis to obtain numeric scores of
relevance for each interaction in this network (i.e. EF values).
First, in order to understand the performance of our approach with
respect to identifying intra- and intercellular networks, we tested
the composition of increasing numbers of EF values for intra-
and intercellular interactions. Figure 3A shows the compositions
of draws of 50–5000 highest EF values (Fig. 3A). Relative
compositions of the individual intracellular interactions are similar,
with intercellular interactions comprising <10% of all interactions,
as expected. Therefore, NetWalk does not bias the scoring of
interactions to one or the other cell type.
We have shown that networks extracted from EF values are
more faithful to the underlying data than other network extraction
algorithms (i.e. high scoring networks contain genes with high
data values and vice versa) (Komurov et al., 2010). We wanted
to test if the EF values from NetWalk analysis of the CMN are
also faithful to the underlying data distribution from the two cell
types. Figure 3B shows the t-value distributions of genes from each
cell type within the networks of 50–5000 highest EF values. The
boxplots show that even in the network of 5000 highest scoring
interactions from this NetWalk analysis, the distribution of t-values
of genes in both cell types is strictly at the higher end of the
individual t-value distributions. We have also conducted additional
randomization simulations to verify the strict coherence of NetWalk
output within the CMN context to the input data (see Supplementary
Fig. S2 and legend). This indicates that the CMN subnetworks
extracted from NetWalk analysis are highly related to the input
data, so that high scoring networks are composed of genes with
high t-values, rather than unrelated genes. This feature of NetWalk
analysis increases confidence in the relevance of the extracted
networks to the underlying data.
First, we analyzed the individual intracellular networks by
visualizing networks corresponding to highest EF values from each
cell type. An individual analysis of the network from highest
EF values in tumor cells shows an upregulation of networks
involved in survival signaling centered around phosphoinositide 3
among others (Fig. 3C). PI3K in this network seems to be
activated by multiple mechanisms, including Toll-like receptor
(TLR2), insulin receptor substrate (IRS2) and G-protein signaling.
Stromal cells, however, are characterized by upregulation of
different networks, primarily in the extracellular matrix (ECM)
remodeling, epidermal growth factor receptor (EGFR) signaling,
oxidative energy metabolism and others (Fig. 3D). Upregulation
of EGFR in stromal cells was also reported in the original
study, suggesting that our approach, in addition to revealing novel
associations, can reproduce previous findings. Although revealing
potentially important information on the respective intracellular
network analyses do not allow for analyses of interactions between
these two networks.
Analysis of individual cell networks
3.3Analysis of the community networks of tumor and
In order to identify community-wide molecular networks involved
in drug resistance in this experimental model, we visualized the
network corresponding to highest scoring 400 EF values (Fig. 4A).
As would be expected from the total percentage of intercellular
interactions in the CMN (∼6%), most of the network is composed
of intracellular interactions of the respective cell types. There are
however a few potentially important interactions occurring between
stromal and tumor cell gene products (highlighted in Fig. 4A).
This subnetwork contains both physical and metabolic intercellular
3.3.1 Physical interactions
interactions include HMGB1 secreted by stromal cells acting on
the TLR2 and PLG (plasminogen) in tumor cells to activate them
(Fig. 4B), NID1–LAMC2 (nidogen and laminin C2) involved in
the ECM remodeling, fibroblast growth factor signaling (tumor
FGF8–stromal FGFR2), tumor cell bone morphogenetic protein
(BMP) proteins acting on the BMP receptor on stromal cells,
chemokine signaling between stromal and tumor cells, and
neuropeptide signaling (neuropeptide (NPY) from stromal cells
activating NPY5R, the receptor, on tumor cells). Upregulation
of FGFR2 in stromal cells was reported in the original study
of Cascone et al., again showing that our analysis can capture
findings from traditional approaches. However in addition, this
analysis shows multiple potentially important intercellular physical
interactions associated with drug resistance. Importantly, most of
these intercellular physical interactions have been described in the
literature to play roles in different aspects of cancer progression.
Some notable examples for physical
3.3.2 Metabolic interactions
the subnetwork in Figure 4Acontains several metabolic intercellular
interactions. A particularly intriguing set of interactions is shown
separately in Figure 4C, which contains several enzymes in
tumor and stromal cells involved in energy metabolism. A
sketch of the reactions performed by these enzymes in the
stromal and tumor cells is shown in Figure 4D. Stromal
cells have a significant upregulation (based on t-value) of the
enzymes lactate dehydrogenase B (LDHB), dihydrolipoamide
dehydrogenase (DLD), glutamic pyruvate transaminase (GPT2),
tumor cells have upregulated lactate dehydrogenase C (LDHC) and
asparaginase like 1 (ASRGL1). While other lactate dehydrogenase
enzymes primarily catalyze conversion of pyruvate to lactate as the
terminal reaction of glycolysis, LDHB mainly converts lactate back
to pyruvate. DLD is a component of several enzyme complexes,
including the pyruvate dehydrogenase complex, which catalyzes
conversion of pyruvate to acetyl CoA, which enters the tricarboxylic
acid cycle (TCA). Similarly, PC catalyzes conversion of pyruvate
to oxaloacetate, which also enters the TCA cycle. Enzyme GPT2 in
stromal cells catalyzes the reversible reaction pyruvate + glutamate
↔ alanine + 2-oxoglutarate, which is one of the important reactions
in gluconeogenesis. ASNS and ASRGL1 catalyze, respectively,
asparagine synthesis producing glutamate from glutamine, and
breakdown of asparagine to aspartate producing NH3.
It is obvious from this picture, that stromal cells have a
significant upregulation of the enzymes involved in oxidative
energy generation (LDHB, DLD, PC) and gluconeogenesis (GPT2),
which is also evident from the significant upregulation of the
cells, however, seem to mainly perform glycolysis, as evidenced
from preferential expression of LDHC and glycolytic enzymes
hexokinase I (HK1), GPI and the glucose transporter SLC2A10 (see
the highlighted subnetwork 9 in Fig. 4A).Three metabolites, lactate,
the tumor and stromal cells in this scenario. While the functional
in tumor cells potentially involving an aspartate/asparagine shuttle
may not be immediately clear, the interaction of LDHC with LDHB
and other stromal enzymes involved in oxidative energy metabolism
is particularly intriguing. Based on the above observations, a
hypothesis emerges where tumor cells primarily consume glucose
by glycolysis and secreting lactate, which is then uptaken by stromal
cells and oxidized through mitochondrial TCAcycle and/or used for
gluconeogenesis to produce glucose or other building blocks (e.g.
by GPT2) that may be used again by tumor cells. This hypothesis is
not far-fetched, as a similar metabolic symbiosis involving lactate
shuttling between stromal and tumor cells has been reported and
has been implicated with increased tumorigenicity in several cancer
models (Koukourakis et al., 2006; Pavlides et al., 2009; Sonveaux
et al., 2008). Therefore, the metabolic interactions identified in this
study may underlie bona fide cross-talk mechanisms between tumor
and stromal cells that play roles in meeting the metabolic demand
of the tumorigenic and drug resistance phenotypes.
In addition to physical interactions,
In this study, we proposed a modeling approach of multicellular
systems as community-wide molecular networks to increase our
Molecular networks of multicellular systems
Fig. 4. CMN analysis of tumor and stromal t-values. (A) Highest scoring interactions from the CMN analysis of tumor and stromal t-values. Nodes are
colored by the respective t-values of tumor and stromal cells as shown in the color key. Interactions are colored as in Figure 3. Some notable subnetworks of
intercellular interactions are highlighted in boxes and numbered. (B)Ablow-up of the network 1 in (A). (C)Ablow-up of the network 7 in (A). (D) Metabolic
reactions performed by enzymes in C in the tumor and stromal cells.
understanding of the complex interplay between intracellular
networks of different cell types within the community. Using
our previously developed data-biased random walk approach,
NetWalk, together with CMN, we were able to obtain a view
of the complex interplay between intracellular networks of tumor
and stromal cells in acquired drug resistance. In addition to
identifying the previously reported findings from the original
study of Cascone et al., our approach uncovered several novel
intercellular interactions involving physical as well as indirect
metabolic cross-talk with potential roles in the drug resistance
phenotype. Although experimental validation of these findings is
beyond the scope of the current study, which is to demonstrate the
use of CMN in conjunction with NetWalk, the analyses presented
here show that the results faithfully reflect the data distribution
that was used as input for NetWalk (Fig. 3B and Supplementary
Although the analyses demonstrated here used NetWalk for
network integration, other methods, such as those based on data
cutoffs could be used.Although insightful community networks can
therefore will not be accounted for during analyses (Supplementary
by NetWalk contain most of the genes with highest data values, and
therefore information loss is minimized in NetWalk-based network
analyses (Supplementary Fig. S3 and legend).
The community network model that we present here takes
into account both physical and indirect interactions mediated by
secreted metabolites between different cells within the community.
well-characterized in several systems, intercellular interactions at
the metabolite level have not been studied to a similar extent
(Lewis et al., 2010), leaving much room for exploration. Our
model enables analyses of large-scale functional genomics data
from multicellular systems for the identification of complex
scenarios involving functional interplay at the physical and
metabolic levels between different cells of the multicellular
system. Overall, our community-wide molecular network model
is an invaluable tool in genome-wide studies of multicellular
We thank Dr Anil Jegga for helpful discussions of the manuscript.
5P30CA149239) to Nancy Ratner.
Thisstudywas partlyfunded bythe (NIH-
Conflict of Interest: none declared.
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