The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.

1. OVERALL - PROJECT SUMMARY
The mission of the National Resource for Network Biology (nrnb.org) is to advance the science of
biological networks by providing leading-edge bioinformatic methods, software and infrastructure, and
by engaging the scientific community in a portfolio of collaboration and training opportunities.
Biomedical research is increasingly dependent on knowledge of biological networks of multiple types
and scales, including gene, protein and drug interactions, cell-cell and cell-host communication, and
vast social networks. The NRNB technologies enable researchers to assemble and analyze these
networks and to use them to better understand biological systems and, in particular, how they fail in
disease. The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since
2010; the present application is a competitive renewal. During the previous funding period, NRNB
investigators introduced a series of innovative methods for network biology including network-based
biomarkers, network-based stratification of genomes, and automated inference of gene ontologies
using network data. We put in place major software and hardware infrastructure such as the
Cytoscape App Store, an online market place of network analysis tools; GeneMANIA, a popular online
network query and gene function prediction tool; cBioPortal, a powerful web portal for analyzing cancer
mutations in a network and pathway context; and a high-performance computing cluster for network
analysis. We established a large portfolio of research collaborations (75 currently active); two annual
international network biology meetings, the Network Biology SIG and the Cytoscape Symposium; and
a rich set of training opportunities and published network analysis protocols. Over the next five years,
we will seek to catalyze major phase transitions in how biological networks are represented and used,
working across three broad themes: (1) From static to differential networks, (2) From descriptive to
predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these
efforts leverage and further support our growing stable of network technologies, including the popular
Cytoscape network analysis infrastructure.

2. OVERALL - PROJECT NARRATIVE
Although we are all familiar with some of the components of biological systems – DNA, proteins, cells,
organs, individuals – understanding life involves more than just cataloging its component parts. It is
critical to understand the many interactions between these parts, and how this complex network of
parts gives rise to biological functions and responses, health and disease. The National Resource for
Network Biology provides the biomedical research community with a shared set of computational tools
for studying a wide range of biological networks, including networks of genes and proteins, networks of
cell-to-cell communication, and social networks of human individuals.

3. OVERALL - RESEARCH STRATEGY
The mission of the National Resource for Network Biology (NRNB; http://nrnb.org/) is to advance
the new science of Biological Networks by providing leading-edge bioinformatic methods, software and
infrastructure, and stimulating the scientific community with a portfolio of collaboration and training
opportunities. Biomedical research is increasingly dependent on knowledge of biological networks of
multiple types and scales, including gene, protein and drug interactions, cell-cell and cell-host
communication, and vast social networks. NRNB technologies enable researchers to assemble these
networks and to use them to understand and predict how biological systems function and fail.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010;
the present application is a competitive renewal. It supports six major components, including:
§ Technology Research and Development of new bioinformatic methods and tools (3 TRDs)
§ Driving Biomedical Projects closely integrated with research and development (10 DBPs)
§ Collaboration and Service Projects within the greater scientific community (70+ CSPs)
§ Software and hardware infrastructure for Network Biology
§ Dissemination of methods, leading research, bioinformatic tools and databases
§ Training via symposia, workshops and a summer code development program
Technology Research and Development of new bioinformatic resources is the central activity of the
NRNB. As a significant deliverable, mature bioinformatic methods developed by each of the three TRD
projects are disseminated as freely available open-source software, many as apps to the Cytoscape
platform, a popular network analysis package that our team initiated in 2001. Each research and
development area is stimulated by an array of collaborators who are generating or using biological
network data within Driving Biomedical Projects. Specific DBPs are chosen to ensure that our
technology development is actively guided according to the needs of the scientific community. Over
time, the selection of active DBPs, and the more distal CSPs, is revised and expanded in consultation
with our External Advisory Committee. These research efforts are solidified by substantial software
and high-performance hardware infrastructure we have put in place over the past several years, along
with a broad program of training and dissemination.
During the previous funding period, NRNB investigators introduced a series of innovative methods for
network biology including network-based biomarkers1,2
, network-based stratification of genomes3,4
, and
automated inference of gene ontologies using network data5-8
. We put in place major software and
hardware infrastructure such as the Cytoscape App Store9
, an online market place of network analysis
tools, cBioPortal10
, a powerful web portal for analyzing cancer mutations in a network and pathway
context, and a high-performance computing cluster for network analysis. We established a large
portfolio of research collaborations (75 currently active); two annual international network biology
meetings, the Network Biology SIG and the Cytoscape Symposium; and a rich set of training
opportunities and published network analysis protocols. All of these efforts leveraged and further
supported our popular Cytoscape network analysis infrastructure.
Technology Themes
As a result of these efforts, we are nearing completion of a vision whereby a coordinated suite of tools
is able to gather public data on biological networks—including molecular, genetic, functional, and/or
social interactions; to visually and computationally integrate these networks with genomics and
proteomics data sets; to semi-automatically assemble all of the data into models of biological
pathways; to suggest points for experimental validation or clinical intervention; and to publish network
models at various stages of refinement online. Although work is still required to mature and iteratively
improve on this vision, the stage is now set to critically evaluate the state of the science and to take on
a new set of challenges.

4. During the next funding period, we will seek to bring about a major phase transition in how biological
networks are represented and used. In consultation with our External Advisors, we have identified
three complementary themes which identify major barriers or areas of need in pushing forward the field
of Network Biology, and for which NRNB is well-positioned to catalyze major change:
A. Differential Networks. In this TRD area, we will develop a series of tools and methodologies for
conducting differential analyses of biological networks perturbed under multiple conditions. The novel
algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover
biological networks under specific biological perturbations. The software tools developed in this project
enable researchers to further predict, analyze, and visualize the effects of these perturbations and
alterations, while enabling researchers to aggregate additional information regarding the known roles
of the involved interactions and their participants.
B. From Descriptive to Predictive Networks. Genomics is mapping complex data about human biology
and promises major medical advances. However, the routine use of genomics data in medical
research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this
TRD theme, we will use network information to help organize, analyze and integrate these data into
models that can be used to make clinically relevant diagnoses and predictions about an individual.
C. Multi-scale Network Representations. Although networks have been very useful for representing
molecular interactions and mechanisms, network diagrams do not visually resemble the contents of
cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein
complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and
so on. In this technology research project, we will pursue methods that move Network Biology towards
such hierarchical, multi-scale views of cell structure and function.
Figure 1. Themes of network-
based research. The NRNB
competitive renewal application is
organized around three major
themes. Two themes advance
technology for systematic assembly
of network models that are dynamic
(Theme I) and multi-scale (Theme
III). These advances in network
representation support the
remaining theme of using such
network knowledge to enable
general biological and biomedical
predictions, such as translation of
genotype to phenotype (Theme II).

5. These three themes serve as the major organizing principles underlying each of the three TRD
projects, along with matching DBPs (Figure 1, Table 1). These themes are timely, as the recent
accumulation of molecular network data in mammals, and the concomitant growth of new sequencing
and other ‘omics technologies, create remarkable new opportunities for using network-based
approaches to understand disease states on a genome-scale in human individuals.
Computational Network Biology: An evolving discipline
Biomedical research is at a critical juncture in which vast amounts of information on molecules and
molecular interactions have been collected, but methods to analyze these data are still in their infancy.
Technologies for interaction mapping are advancing at a staggering pace. Experimental approaches
include yeast two-hybrid assays11-19
, affinity purification coupled to mass spectrometry20-29
, reverse
PCA30
, chromatin immunoprecipitation of transcription factor binding31-42
, chromatin conformation
capture of DNA-DNA interactions43-45
, synthetic-lethal and epistatic networks46-57
, expression
quantitative trait loci58-66
, and many others. Apart from the molecular level, biological networks of other
types are central to a range of biomedical disciplines. For example, vast social networks connecting
friends, co-workers, and family members are being charted to lend insight into how disease is
transmitted67-70
. Such networks are being powerfully integrated with data from systematic profiling
experiments, such as changes in mRNA or protein abundance71-73
, protein phosphorylation state74
and
metabolite concentrations75
quantified with mass spectrometry, and other advanced techniques.
This enormous collection of networks and profiles necessitates bioinformatic frameworks to integrate,
filter, visualize, and interpret the data. A wide range of techniques are being developed to analyze
network data to address specific problems with relevance to basic biology and human health:
⇒ Gene function prediction. Since many genes still have unknown functions, examining genes in a
network context can provide important functional clues. For example, a protein connected to a set
of proteins involved in the same biological process is likely to also function in that process76-78
.
Table 1: Matrix of technology development and matching biomedical projects. Matrix view of
Technology Research and Development project aims (rows) grouped by themes (colors) and Driving
Biomedical Projects (columns) also grouped by themes (alternating gray), which are labeled in bottom header
row. A matrix cell is filled with a color corresponding to the Technology Theme for every case where a
particular DBP is driving a particular TRD aim. Each Technology Theme is driven by multiple biomedical
projects; 1-6 DBPs are associated with each TRD aim. Similarly, each Driving Biology Project is matched to
1-4 TRD aims, often across themes.

6. ⇒ Detection of protein complexes and other modular structures. There is clear evidence in biological
networks for modularity and principles of higher-order organization79,80
. For example, molecular
interaction networks have been found to cluster into regions that represent complexes81
, and
statistically over-represented motifs, such as feedback loops, have been discovered82
some of
which have been thoroughly analyzed83,84
.
⇒ Network evolution. Increasingly, the study of evolution is moving to the network level. A number of
techniques have been developed that identify common network structures and organizational
principles that are conserved across species85-88
, including our PathBLAST89
and NetworkBLAST90
tools which align protein networks to identify conserved interaction paths and clusters.
⇒ Prediction and further specification of interactions. Statistically significant domain-domain
correlations in a protein interaction network suggest that certain domains (and domain pairs)
mediate protein binding, allowing prediction of new interactions and binding interfaces91,92
. Machine
learning has also been applied to predict protein-protein or genetic interactions through integration
of diverse types of evidence for interaction93,94
.
⇒ Identification of disease subnetworks. A recent and powerful trend is to identify neighborhoods of a
biological network (i.e., subnetworks) that are transcriptionally active in disease95-99
. Such
neighborhoods suggest key pathway components in disease progression and provide leads for
further study and potential new therapeutic targets.
⇒ Subnetwork-based diagnosis. It has become clear that subnetworks also provide a rich source of
biomarkers for disease classification. Several groups3,100-103
have demonstrated that mRNA profiles
can be integrated with protein networks to identify subnetwork biomarkers—i.e. subnetworks of
interconnected genes whose aggregate expression levels or nucleotide variants are predictive of
disease state.
⇒ Subnetwork-based gene association. Despite the growing number of gene-disease association
studies104-106
based on Single Nucleotide Polymorphism (SNP) and Copy Number Variation (CNV)
profiling, most human genetic variation remains uncharacterized, both in biological mechanism and
in impact on disease pathology. We anticipate that molecular networks will continue to provide a
powerful framework for mapping the common pathway mechanisms impacted by a collection of
genotypes, as several proofs-of-principle have been developed in this area3,107-114
.
Major Changes from Prior Period of Support
NRNB has progressed in important ways since the initial application was funded in 2010. Numerous
minor, and several major, changes to the organization and focus of the resource were prompted during
regular reviews by our very engaged External Advisory Committee (2011, 2012, and 2014 meetings).
Generally, the EAC has found that “scientific progress has been impressive… [as highlighted by] the
spectrum of recent publications” and that “the collaboration and outreach strategy developed by the
NRNB has been very effective to make available and accessible network biology methods to those
researchers less versed in it” (written EAC report, July 12, 2014). They also have made constructive
critiques which were taken seriously and catalyzed changes in trajectory. Below are highlights of
critiques summarized from the progression of EAC meetings, followed by our corresponding actions:
Year-to-year milestones should be tracked more carefully as each TRD aim progresses.
At the suggestion of the EAC, we now track each TRD aim along a progression of four stages: [1]
Identification of a resounding need, [2] Research leading to a prototype method or proof-of-principle,
[3] Transition to a robust end-user tool; [4] Dissemination and collaboration to a wide audience.
Focus not only on network visualization but on network-based analysis and decision making.
Exciting progress was made for using networks as diagnostic biomarkers under original TRD A1,3,115
.
This enabled us, in the renewal, to seed an entire Theme 2 (TRD 2) on ‘predictive’ networks.
Encourage increased citation of NRNB tools such as Cytoscape. Track classroom use of Cytoscape.

7. We refactored nrnb.org and cytoscape.org websites to encourage citation and to better capture both
citations and non-cited usage. We identified 32 courses worldwide using Cytoscape (2011 – 2014).
Better track Cytoscape plugins and do more to encourage developers to write them.
We rebranded plugins as ‘Apps’ and obtained an NRNB award supplement allowing development of
the Cytoscape App Store (apps.cytoscape.org) which has greatly stimulated App development and
use.
Transition away from paper counts and toward metrics of community enablement and integration.
We now track metrics such as number of NIH grants using NRNB tools (3800!), third-party software
development we enabled (>200 Cytoscape Apps in App Store), and number of Google and NRNB
Academy mentees (59). We routinely solicit qualitative ‘testimonials’, of which we have received close
to 100.
Connect to synergistic resources like GenomeSpace and IGV and enable network sharing on the Web.
Cytoscape is now connected to RBVI's Chimera, Sage Bionetworks’ Synapse, GenomeSpace (a CSP
which transitioned to independent funding from NHGRI, PI Jill Mesirov), IGV and the Cancer Gene
Encyclopedia. We can now export networks to these and other resources using cytoscape.js and d3.js.
Optimize existing TRDs and DBPs to focus the core NRNB strengths. Include new, more distal DBPs.
In the renewal, TRDs have been organized around three themes which capture major challenges in
Network Biology with significant potential impact. We have added key DBP sites including Sage
Bionetworks (Stephen Friend), Dana Farber (Marc Vidal), and Stanford (Mike Cherry). These changes
in TRDs and DBPs have also prompted changes to our key personnel: James Fowler, who formerly
ran a TRD related to social network analysis, is now positioned as a social networks DBP, which
allows general network analysis methodology in multiple TRDs to impact social networks as one
driving project alongside other biological network types. We have transitioned former TRD D, related to
gene regulatory network inference, into a DBP with collaborators from Sage/DREAM.
NRNB Leadership and Administration
NRNB is led through the coordinated efforts of a team of four pre-eminent faculty in Network Biology
and Bioinformatics: Dr. Trey Ideker (University of California San Diego), Dr. Alexander Pico (Gladstone
Institute / University of California San Francisco), Dr. Gary Bader (University of Toronto), and Dr. Chris
Sander (Memorial Sloan Kettering Cancer Center). These investigators are well-known thought
leaders in the field with strong track records of innovative methods development and collaboration in
network biology tools and applications. NRNB administration is based at the University of California
with Dr. Ideker as Director and Alexander Pico as Executive Co-Director. Ideker and Pico together
coordinate activities among host institutions under a Consortium/Contractual arrangement with UCSD.
The four leaders are distributed across major biomedical research sites spanning both U.S. coasts and
Canada. This arrangement positions NRNB as a truly national and international organization with
broad exposure to, and involvement in, a spectrum of Driving Biomedical Projects and opportunities for
Collaboration and Service. Because NRNB expertise is geographically distributed, this allows each
NRNB site to harness the best local talent as well as to stimulate and respond to collaborative projects
within their local research communities. This is an inversion of the usual arrangement for resource
centers, in which the center is geographically localized and reaches out to investigators located
elsewhere. In contrast, NRNB is already global and thus “thinks globally, acts locally.”
NRNB Director, Dr. Trey Ideker’s credentials stem from a career of pioneering work in systems biology
and a very strong track record of creating innovative and widely-used bioinformatic tools. Ideker
received bachelor’s and master’s degrees in computer science from MIT and a PhD in biomedical
sciences working with Dr. Lee Hood at the University of Washington. Since 2001 Ideker has led an
independent research laboratory first as a Whitehead Fellow then UCSD professor, under continuous
NIH funding. Ideker’s research is led by the vision that, given the right experimentation and analysis, it

8. will be possible to automatically assemble maps of pathways just as we now assemble maps of
genomes. During graduate work, he developed a general iterative framework for how biological
systems can be systematically perturbed, interrogated and modeled. This framework laid the
foundation for many studies in the future discipline of Systems Biology116,117
. Using his new Cytoscape
platform, he demonstrated that biological networks could be integrated with gene expression to
systematically map pathways118
and aligned, like sequences, to reveal conserved and divergent
functions89,119-123
. He showed that the best biomarkers of disease are typically not single proteins but
aggregates of proteins in networks100
. Ideker was named a Top Ten Innovator of 2006 by Tech.
Review and received the 2009 Overton Prize, the highest award in computational biology. He serves
on the Editorial Boards of Cell, Cell Reports, Molecular Systems Biology, and PLoS Comp. Bio.
Directing NRNB builds on previous successful leadership such as Director of the San Diego Center for
Systems Biology, Genome Program Leader at the UCSD Cancer Center, former Division Chief of
Genetics in the Dept. of Medicine, and as a regular organizer of major conferences such as Cold
Spring Harbor Laboratory Systems Biology: Networks and the annual Protein Networks Track at ISMB.
NRNB Executive Director, Dr. Alexander Pico is the Associate Director of Bioinformatics at the
Gladstone Institutes on the UCSF Mission Bay Campus where he leads a Systems Biology Group.
After obtaining a PhD in molecular neurobiology and biophysics from Rockefeller University in 2003,
Dr. Pico joined Gladstone as a postdoctoral fellow in the laboratory of Dr. Bruce Conklin. Dr. Pico
develops software tools and resources that help analyze, visualize and explore biomedical data in the
context of biomolecular networks. He cofounded WikiPathways in 2007 and has contributed to the
development of Cytoscape since 2006. Dr. Pico received the David Rockefeller Award in 2001, the
Howard Hughes Medical Institute Predoctoral Fellowship from 1999-2003, the David and the Mary
Phillips Distinguished Postdoctoral Fellowship for 2005-2006.
NRNB Investigator, Dr. Gary Bader works on biological network analysis and pathway information
resources as an Associate Professor at The Donnelly Centre at the University of Toronto. He helped
found the field of network biology with early contributions to network visualization and modeling124-127
,
database resources128
and analysis81
. He was included twice (for Biology/Biochemistry and Computer
Science) on the Thomson Reuters list of most influential scientists worldwide in 2014
(www.highlycited.com). His research focuses on identifying causal pathways in disease and in
precision medicine (predicting patient outcome based on genetic, molecular, pathway and clinical
data). For example, using his methods, his team identified novel pathways involved in autism spectrum
disorder129
and the first potential therapy for a devastating pediatric brain cancer130
. He completed
post-doctoral work in Chris Sander’s group in the Computational Biology Center at Memorial Sloan-
Kettering Cancer Center in New York. Gary developed the Biomolecular Interaction Network Database
(BIND) during his Ph.D. in the lab of Christopher Hogue in the Department of Biochemistry at the
University of Toronto and the Samuel Lunenfeld Research Institute at Mount Sinai Hospital in Toronto.
He completed a B.Sc. in Biochemistry at McGill University in Montreal.
NRNB Investigator, Dr. Chris Sander is Chair of the Computational Biology Program at the Memorial
Sloan–Kettering Cancer Center in New York City. Sander was initially trained as a physicist, with a first
degree in physics and mathematics from the University of Berlin in 1967, and a PhD in theoretical
physics after graduate studies at the State University of New York, the Niels Bohr Institute in
Copenhagen and the University of California in Berkeley. Sander has made many contributions to
structural bioinformatics including developing tools such as FSSP and DSSP. DSSP, Sander’s
foundational algorithm for analyzing protein structures, is still used 17 years later as a standard for
identifying protein secondary structures in experimental 3D structures. In 1986, he became department
chair and founder of the Biocomputing Program at the European Molecular Biology laboratories
(EMBL) in Heidelberg with the support of Sydney Brenner. He was a founding member of the
International Society for Computational Biology (ISCB) when the society was established in early 1997,
and he is a former Executive Editor for the journal Bioinformatics. In 2010 he was awarded the ISCB
Accomplishment by a Senior Scientist Award.

9. External Advisory Committee. The above leadership team is co-advised by an External Advisory
Committee (EAC) which has historically been very active. The EAC consists of:
Dr. Stephen Friend, Founder and President of Sage Bionetworks (EAC Chair)
Dr. David Hill, Associate Director of the Harvard Center for Cancer Systems Biology
Dr. Tamara Munzner, Professor of Computer Science at University of British Columbia
Dr. Nik Schork, Director of Human Biology, J. Craig Venter Institute
Dr. Gustavo Stolovitzky, Systems Biology Group Leader at IBM/Watson Research Center
Dr. Marian Walhout, Professor of Molecular Medicine at University of Massachusetts
Dr. Ravichandran Veerasamy, NIGMS Program Representative
Each of these members brings relevant, world-renowned expertise and leadership in functional
genomics (Friend), protein networks and interactomics (Walhout, Hill), human genetics (Schork),
information visualization (Munzner), and inference of signaling and transcriptional regulatory networks
(Stolovitzky founded the well-known DREAM competition). The EAC met with NRNB leadership and
investigators to review progress at three separate face-to-face meetings during the past funding
period. The EAC also plays a critical role in the continual evolution of DBPs and CSPs, by suggesting
new collaborative opportunities in the field and approving all substantive changes to this lineup.
Projects are evaluated based on their ability to yield joint publications between resource personnel and
their collaborators, with a strong preference towards DBPs that are already NIH funded. For the DBPs
described in this proposal, all are already supported separately by the NIH or Genome Canada.
Size of the Research Community to be Served by NRNB
The community of scientists who might be considered “network biologists” has grown rapidly over the
past few years. As an illustration of how pervasive networks have become, the U.S. National Institutes
of Health currently funds 19,759 active research grants covering the topic “protein protein interactions”
which, remarkably, is up from 3,076 at the time of the last NRNB submission four years ago in fall
2010. Network research is also actively supported by a variety of initiatives from other major national
and supra-national funding agencies, such as the NSF, DOE, EU, and private foundations. As of 2014,
approximately one-third of papers presented at the major Bioinformatics conferences (e.g., ISMB,
RECOMB, ECCB) have focused on novel methodologies related to analysis or inference of biological
networks— this number is up from less than one tenth a decade ago. Network Biology is the focus of
major conferences per year, recently including the “Systems Biology: Networks” meeting (March 13-16
2013, CSHL); “Computational analysis of protein-protein interactions” (Sept 29 - Oct 3 2014; organized
by EMBO), and “NetBio” (July 1 -15 2014, organized by NRNB staff at ISMB).
Another indicator of the growing community of network research is usage of software tools. For
instance, use of Cytoscape has increased at a rate of ~50% per year since 2003. In the past year
alone, Cytoscape has been downloaded ~90,000 times and the website has had 370,000 sessions
and 880,000 pageviews. The NRNB-built and maintained App Store has delivered over 170,000 apps
to Cytoscape users since its launch in mid-2012. The original Cytoscape paper131
and the more
recently released update papers131-133
have together been cited over 12,000 times and used un-cited in
many more. Cytoscape analyses appeared in ~50 Science/Nature/Cell articles in 2013. Figure 2
shows how a subset of these works has been supported by a collection of different NIH institutes as
well as other funding agencies. These increases in software usage and development closely parallel
the increases in availability of large-scale network information and its potential impacts in biomedicine.

10. Thus, the importance of Network Biology is very much appreciated by basic scientists and clinicians,
many of whom use interaction databases and network analysis tools in their day-to-day research. On
the other hand, many other investigators (basic, clinical, and pharmaceutical) as of yet have little
knowledge of recent developments in network biology and would benefit greatly from outreach and
education centered on network-based technologies. With continued support of the NRNB National
Resource— focused on technology development supporting research, collaboration, education, and
dissemination— the Network Biology community could potentially become a great deal larger.
Progress Report Highlights
The initial period of support (Sept 2010 - Aug 2015) has led to the establishment of a Network Biology
resource which provides a unique and fertile environment for bioinformatics research, tool
development, dissemination, collaboration and training. The initial years have been very productive by
both quantitative metrics (cumulative) and research narrative:
Total publications: 83 total, 16 in Nature / Science / Cell family; 63 collaborative with ≥ 2 faculty
Cytoscape gets 8,000 dls/mo, 500 cites/yr and has supported 3,800 NIH grants
Number of network analysis Apps in Cytoscape App Store: 210
149 collaborations with external investigators on diverse topics (75 active)
92 training events in 12 countries, including 15 course lectures by NRNB staff
70 Google Summer of Code projects = $304,000 invested by Google
Workshops: 39 total, ~1,000 attendees
Symposia: 7 total, ~700 attendees
The below narrative highlights major progress under each component of the resource, including new
technology development, infrastructure, collaborative activities, dissemination, and training:
New Technology: Network Approach to Building Gene Ontologies. NeXO (The Network Extracted
Ontology) uses a principled computational approach which integrates evidence from hundreds of
thousands of individual gene and protein interactions to construct a complete hierarchy of cellular
components and processes5,7
. This data-derived ontology aligns with known biological machinery in
the GO Database and also uncovers many new structures (Figure 3).
NIGMS, 1005
NCI, 459
NCRR, 275
NIAID, 260NIDDK, 210
NHLBI, 202
NHGRI, 164
NIEHS, 157
NLM, 117
NIMH, 97
Wellcome Trust, 95
NINDS, 89
Canadian Institutes of
Health Research, 85
NIDA, 72
Others, 541
Figure 2: Use of Cytoscape
by grantees of funding
agencies. We have been
able to identify at least 3,800
active grants that depend on
Cytoscape, up from ~1,500
18 months ago. Information
was extracted from the
Acknowledgments section of
journal articles. One pie slice
represents the percent of
this total support provided by
a particular agency. Over
90% of grants are by NIH
institutes.

11. New Technology: Network-based stratification of tumor
mutations. NBS is a method for stratification (clustering) of
patients in a cancer cohort based on somatic mutations and
a gene interaction network3
. The method uses network
propagation to integrate genome wide somatic mutation
profiles for each patient over a gene interaction network,
and a non-negative matrix factorization based clustering
approach in order to derive biologically meaningful
stratification of a patient cohort.
Key Infrastructure: Cytoscape. Our team has worked very
closely together since 2002 to develop Cytoscape, a
standard visualization, integration and analysis package for
biological networks (http://www.cytoscape.org/). Our
previous Cytoscape development efforts, which predate
NRNB, demonstrate the strong commitment of our
leadership team to open-source bioinformatic tools. Our
ability to establish a strong resource center owes in large
part to the groundwork we have put in place over the
previous decade of Cytoscape development. During the
funding period, NRNB helped expand the Cytoscape
desktop application into a suite of related tools, Apps,
websites and databases which we propose to call the
Cytoscape Cyberinfrastructure.
Key Infrastructure: Establishment of the NRNB Compute Cluster. Over the performance period we
worked closely with the San Diego Supercomputer Center (SDSC) to establish a high-performance
computing environment for network biology applications. Through NRNB funds and matching SDSC
support, we constructed an advanced cluster centered on 512 cores (64GB and 256GB HP SL230s
Gen8 blade nodes) with 128TB attached storage. This infrastructure has been highly used and critical
for new technology development, as manipulation, search, and statistical scoring of large network
structures is typically compute intensive. We propose to renovate and expand this infrastructure in the
next funding period.
Dissemination: The Cytoscape App Store. A highlight of NRNB Dissemination efforts is the Cytoscape
App Store (http://apps.cytoscape.org/) which was developed under supplemental funding to the main
NRNB award. The goals of the App Store are to highlight the important features that apps add to
Cytoscape, to enable researchers to find apps they need, and for developers to promote their apps. It
has stimulated a sizable community of Cytoscape App developers, enabling over 200 third-party
network analysis and visualization tools to-date.
Dissemination: The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for
interactive exploration of multi-dimensional cancer genomics data sets, currently providing access to
data from more than 5,000 tumor samples from 20 cancer studies10
. The cBio Cancer Genomics Portal
empowers researchers to translate these rich data sets into biologic insights and clinical applications,
for instance by viewing the molecular network context of cancer mutations using cytoscape.js for the
web. Following its introduction in 2012, the portal has been used by thousands of basic cancer
researchers, and increasingly clinicians, wishing to understand the prevalence, biological context, and
outcome of a set of tumor mutations.
Key DBP Collaboration: Synthetic Genetic Analysis of Budding Yeast. NRNB investigator Gary Bader
had a very productive DBP with scientists at the University of Toronto (Charles Boone, Brenda
Andrews) leading to the first complete genetic interaction network for a cell48,134
. This reference map
provides a model for expanding genetic network analysis to higher organisms and is stimulating
Figure 3. Journal cover showing top level
view of the Network-Extracted Ontology
(NeXO), a completely data-driven
reconstruction of the literature-based GO.

12. Figure 5. Differential genetic interactions specifically
highlight pathways responding to DNA damage. Genetic
interaction module map in which protein complexes / pathways
(nodes) are connected by differential genetic interactions
(edges). “Differential positive” scores indicate genetic interactions
that increase after exposure to MMS (green) while “differential
negative” scores indicate interactions that decrease (red).
valuable insights into gene function and
drug targets. The 2010 publication of
roughly 20% of the complete map was
in the top 30 most cited papers of that
year (Figure 4).
Key CSP Collaboration: Genetic
Networks Remodeled by DNA
Damage. Although cellular behaviors
are dynamic, the networks that govern
these behaviors have been mapped
primarily as static snapshots. To
explore network dynamics, Ideker
collaborated with Nevan Krogan at
UCSF to develop an approach called
differential epistasis mapping, which
creates a genetic network (Figure 4)
based on changes in interaction
observed between two static
conditions. They mapped widespread
changes in genetic interaction as the
cell responds to DNA damage57
. Differential networks chart a new type of genetic landscape that will
be invaluable for mapping many different cellular responses to stimuli.
Training: Google Summer of Code. One of our most successful training activities is our participation as
a mentoring organization in the Google Summer of Code program. Over the past 4 years, we have
recruited over 50 mentors and attracted over 100 students around the world to apply to work on
network biology projects. For example, Google funded 18 students recruited by NRNB in 2014, ranking
us in the top 10% in terms of the number of slots allocated by Google across all accepted
organizations (comparable interest to Apache, Mozilla, R, and Python). Our overall application success
rate is 94% on GSoC projects. These
training efforts have resulted in 10
student co-authored papers9,135-143
.
Training: RECOMB/DREAM Conference
and Challenges. Another highlight of our
training efforts has been to create an
annual international symposium on
bioinformatic methods for Network
Biology. Formerly this was the
Cytoscape Symposium, a three-day
annual meeting that attracted 200-300
attendees each year. In 2013 we began
discussions with the well-known
RECOMB (Research in Computational
Molecular Biology) and ISCB
(International Society for Computational
Biology) conference families to combine
efforts. These discussions led to an
agreement to expand the existing
RECOMB/ISCB conference series on
Systems and Regulatory Biology
(originally co-founded by Trey Ideker,
Ron Shamir, and Eleazar Eskin in 2003)
to integrate sessions including a
Figure 4. The genetic interaction map of budding yeast, as
visualized by a DBP between NRNB and the Boone laboratory.

13. Cytoscape App Expo and Cytoscape user and developer training events. At the same time, NRNB
partnered with the popular DREAM Challenge, an annual competition that nucleates thousands of
participants to address important problems in inference and validation of network biology models.
DREAM is also co-organized with the RECOMB conference and, since 2013, the winning network
inference method(s) have been implemented as Cytoscape Apps by the donated time of an NRNB
software developer.
Future Progress Assessment and Evaluation
NRNB progress will be measured using both narrative and numerical information. All investigators
provide annually a narrative of research progress and future plans within each of the TRDs and
coupled DBPs. We maintain a database of bibliographic data that tracks the number of peer-reviewed
publications per TRD and investigator. We track the number of citations to these papers along with
near-term metrics such as the ‘Altmetric’, which indicates rapid online interest in a paper even before
enough time has passed to garner follow-on citations.
Beyond publications, an important NRNB aim is production and maintenance of cutting-edge software
tools, websites, and platforms. These resources will be posted to our website in a manner consistent
with NIH policies (see Resource Sharing Plan), and we will track usage statistics of all new and
existing NRNB-supported resources (e.g. cytoscape.org, apps.cytoscape.org, nexontology.org). We
will also track tools developed by third parties, such as Cytoscape Apps, which are enabled or
supported by core NRNB efforts.
Success of Collaboration and Service Projects is measured by the overall number, complexity, and
quality of ongoing CSPs we manage and the publications stemming from these collaborations. We
also routinely solicit qualitative feedback about CSPs and tool usage in the form of user testimonials.
Products of training and outreach, such as workshops, symposia, and summer training programs like
Google Summer of Code, are tracked via event descriptions and post-event surveys and metrics such
as the number of events and attendees.
Distinction Between NRNB And Existing Resources
NRNB complements other bioinformatic resources and centers, while having unique aims and
deliverables. The following is a partial list of other NIH-supported bioinformatics efforts that are distinct,
but synergistic, with NRNB:
The Cytoscape Project. Cytoscape is a software platform on which to provide tools and infrastructure
for biological network analysis. The core codebase of the Cytoscape desktop application is supported
by a research project grant from NIGMS (R01 GM070743 to Ideker). In contrast, NRNB provides
training and outreach on Cytoscape and its apps, and TRD projects use the Cytoscape platform for
analysis and development of new tools as Apps. NRNB also supports a range of other tools and
databases such as Wikipathways, Enrichment Map, NeXO, the Cytoscape App Store, and
Cytoscape.js/web.
Resource for Biocomputing, Visualization, and Informatics (RBVI) directed by Thomas Ferrin at UCSF.
RBVI is another informatics BTRR that develops software for protein structural analysis and
visualization, including Chimera. This resource has already developed the StructureViz144
Chimera to
Cytoscape App, which links protein networks analyzed in Cytoscape to 3D structures of proteins145,146
.
National Resource for Cell Analysis and Modeling (NRCAM) directed by Leslie Loew at University of
Connecticut. The NRCAM is another informatics BTRR that develops the VCell modeling tool147
which
enables precise physico-chemical simulations of pathway dynamics given information on kinetic
parameters, cellular sub-structure, and localization. In contrast, the focus of NRNB is to integrate

14. heterogeneous interactions and genome-scale data sets together into consolidated pathway maps of
the cell. These maps may be useful as starting points for building simulation models using VCell.
NIGMS Centers for Systems Biology. NRNB has productive interactions with NIGMS Systems Biology
Centers, including the San Diego Center for Systems Biology (SDCSB). The SDCSB brings together
18 systems biology faculty at UCSD, Salk, and surrounding institutes, organized into project teams
who apply systems approaches to advance our understanding of fundamental cellular processes,
including chromatin architecture and transcription, protein turnover, and cell-cell communication. In
2013 the original PI Alexander Hoffman moved to UCLA and passed leadership to Ideker at that time.
Annotated Timeline
The goal of the annotated timeline is not to set explicit milestone dates (per FOA instructions), but
rather to provide target dates for technology availability and to illustrate how the various sub-project
aims related to each other. We are using Munzner's 4-Stage Model to show the preliminary work (gray
bars) and the proposed extension (open bars) for each project (each row) in the context of an
abstracted technology development cycle. Our goal is to push each project to either Stage 3
(implemented tool) or Stage 4 (widely adopted tool) during the next support period. Target dates per
project are provided right-justified within each open bar.
TRD NRNB Lead Specific Aim or Subaim
1.1 MSK / Sander Tools for inference of differential networks
- Integrated tools for perturbation response analysis
- Predictive power using tissue-specific priors; PERA 2.0
- UX and visualization for network comparison
1.2 Toronto / Bader Protein network alignment algorithm and viewer
- IIN network alignment algorithm (GreedyPlus)
- Develop network alignment viewer YEAR 1
- Accessible IIN data via PSICQUIC web service YEAR 1, DBP2
1.3 Gladstone / Pico Facilitating the interpretation of AP-MS data as interaction networks
- Tools for AP-MS import and assessment YEAR 1
- Tools for network augmentation and comparative analysis
2.1 Toronto / Bader Predicting clinical outcome using patient similarity networks
- Outcome prediction methods
- Patient population stratification
- Patient similarity network visualization
2.2 UCSD / Ideker Predicting cellular response to perturbation w/ network-guided regression
& MSK / Sander - Predicting cellular response to treatment using molecular profile data
- Predicting cellular response to treatment using diverse, integrated data
2.3 Gladstone / Pico Network analysis of genetic variant data
- Variant analysis and visualization tools
- Variant data and annotation tools
3.1 UCSD / Ideker Data-driven assembly & refinement of gene ontologies from networks
- Inference of gene ontologies from network data
- NeXO (nexontology.org) data-driven ontology web browser ! YEAR 1
- Procedures for integrating datasets & iterative ontology improvement !
3.2 UCSD / Ideker Functionalized gene ontologies as a hierarchy of functional prediction
- Procedures for propagation of state on gene ontologies
3.3 Toronto / Bader Bridging ligand-receptor networks to cell-cell communication networks
- Cell-cell interaction network inference
- Identify key players and rules
- Intracellular pathway control
- Mutl-scale visualization technology
Java tool YEAR 2 - 3, DBP 9 YEAR 3, CSPs
YEAR 4 - 5, CSPs
YEAR 4 - 5, CSPs
YEAR 3 - 4, CSPs
YEAR 4 - 5, CSPs
YEAR 4 - 5, CSPs
YEAR 4 - 5, CSPs
YEAR 3, CSPs
YEAR 3, CSPs
YEAR 4 - 5, CSPs
Perturbation biology method
PERA algorithm YEAR 1 - 2, DBP 7,8
YEAR 2 - 3, DBP 7,8
'Siri' essay in Cell YEAR 1 - 3, DBP 3, 4 YEAR 3 - 5, DBP 3, 4
NeXO and CliXO Algorithms
NeXO Web Browser
YEAR 2 - 3, DBP 1 - 5
YEAR 1 - 2, DBP 1 - 5,10
YEAR 3 - 4, DBP 1 - 5,10
Identify problem, driving
biological question, and
target community
Develop new method or
approach as a solution;
establish proof of concept
Implement solution as a
mature resource (software,
website, DB)
Broad adoption of resource
via dissemination and
collaboration
Stage 1 Stage 2 Stage 3 Stage 4
YEAR 2 - 3, CSPs
Morris(2014) Nature Protocol YEAR 2 - 3, DBP 1
YEAR 2 - 3, DBP 1Web service resources
PSICQUIC hosted web services
Prototypes & mock-ups YEAR 1, DBP 8
YEAR 1 - 3, DBP 2, 10
YEAR 2 - 4, DBP 1,2,7,8,10
Prototype Java tools
Beta rendering system
Optimize prototype
Mock-up YEAR 2 - 3, DBP 4,5,6 YEAR 3, DBP 4,5,6,10 YEAR 4, CSPs
YEAR 3, CSPs
YEAR 2 - 3, DBP 5
YEAR 2 - 3, DBP 4,5,6,10Java tool
YEAR 3 - 4, DBP 4,5,6,10 YEAR 4 - 5, CSPs
Regression method
Extension to new data
YEAR 1 - 3, DBP 8 YEAR 3, DBP 4,8
YEAR 1 - 3, DBP 8 YEAR 4, DBP 4,8
Cytoscape Integration
Cytoscape Integration
YEAR 2 - 3, DBP 4,5,6,8 YEAR 3, CSPs
YEAR 3 - 4, DBP 4,5,6,8 YEAR 4, CSPs
YEAR 4 - 5, CSPsYEAR 2 - 3, DBP 1,2
YEAR 4, DBP 9 YEAR 5, DBP 9Visualization app
Pathway analysis
YEAR 3, CSPs
YEAR 4, CSPs
YEAR 5, CSPs
Topology analysis YEAR 2, DBP 9 YEAR 3, DBP 9
YEAR 3, DBP 9 YEAR 4, DBP 9
YEAR 4, CSPs
YEAR 5, CSPs

15. OVERALL - SPECIFIC AIMS
The mission of the National Resource for Network Biology is to advance the science of Biological
Networks by providing leading-edge bioinformatic methods, software and infrastructure, and by
engaging the scientific community in a portfolio of collaboration and training opportunities. Biomedical
research is increasingly dependent on knowledge of biological networks of multiple types and scales,
including gene, protein and drug interactions, cell-cell and cell-host communication, and vast social
networks. NRNB technologies enable researchers to assemble and analyze these networks and to use
them to better understand biological systems and, in particular, how they fail in disease. NRNB has
been funded as an NIGMS Biomedical Technology Research Resource since 2010.
Our overall mission is accomplished through the following five Specific Aims:
Specific Aim 1. To mount aggressive programs of research in cutting-edge bioinformatic
technology to address major challenges and opportunities in Network Biology. In past years, we
introduced a series of innovative methods including network-based biomarkers and network-based
stratification of genomes. In the next support period, we will research approaches to represent and
analyze network architecture across conditions or times; a general engine for genotype-to-phenotype
prediction using network knowledge; and a platform to crowd-source construction of a gene ontology
based wholly on network data from the community. Research proceeds in frequent communication
with Driving Biomedical Projects, which provide data and applications from the labs of our close
collaborators.
Specific Aim 2. To catalyze phase transitions in how biological networks are represented and
used in biomedical research. We are well-positioned to catalyze change along three complementary
themes: I. Moving from static network data and models to networks that are differential or dynamic, II.
Moving from networks that are primarily descriptive to those that are predictive of a range of
phenotypes and behaviors, and III. Moving from flat networks (lists of pairwise interactions) to multi-
scale representations that capture the hierarchy of modules comprising a biological system and
reflected in its data.
Specific Aim 3. To establish and disseminate robust end-user software, databases and high-
performance computing infrastructure that enable network analysis and visualization methods for a
broad biomedical research community. We will further develop the popular Cytoscape desktop
application and App Store database of network analysis tools into a mature platform for network-based
research on the web and in the cloud, to be called the Cytoscape Cyberinfrastructure. We will grow the
collection of supported network tools distributed through nrnb.org. And we will update and expand the
current NRNB computing hardware to keep pace with the growing size of network datasets and
computing tasks.
Specific Aim 4. To engage with leading biomedical investigators in productive collaborations
uniquely enabled by NRNB methods and tools. Since our inception in 2010, the NRNB has maintained
an active and rolling portfolio of approximately 60-80 Collaborative and Service Projects. We will use
best practices learned during this initial period of support to continue to acquire, triage, and/or
complete collaborative projects, with an increasing focus on deploying new network methodologies
developed under the three major technology themes outlined in Aim 2.
Specific Aim 5. To train the current and next-generation of biomedical investigators in the
science of Network Biology and its applications in disease research. The NRNB runs a highly attended
annual network biology symposium, which starting in 2014 is planned jointly with DREAM and
ICSB/RECOMB. We run a broad collection of network biology workshops and training events,
including a very popular Google Summer Of Code program which over the past 4 years has recruited
over 50 mentors and 100 students around the world to work on network biology projects under
matching support from Google.

16. OVERALL - PROJECT NARRATIVE
Although we are all familiar with some of the components of biological systems – DNA, proteins, cells,
organs, individuals – understanding life involves more than just cataloging its component parts. It is
critical to understand the many interactions between these parts, and how this complex network of
parts gives rise to biological functions and responses, health and disease. The National Resource for
Network Biology provides the biomedical research community with a shared set of computational tools
for studying a wide range of biological networks, including networks of genes and proteins, networks of
cell-to-cell communication, and social networks of human individuals.

17. OVERALL - SPECIFIC AIMS
The mission of the National Resource for Network Biology is to advance the science of Biological
Networks by providing leading-edge bioinformatic methods, software and infrastructure, and by
engaging the scientific community in a portfolio of collaboration and training opportunities. Biomedical
research is increasingly dependent on knowledge of biological networks of multiple types and scales,
including gene, protein and drug interactions, cell-cell and cell-host communication, and vast social
networks. NRNB technologies enable researchers to assemble and analyze these networks and to use
them to better understand biological systems and, in particular, how they fail in disease. NRNB has
been funded as an NIGMS Biomedical Technology Research Resource since 2010.
Our overall mission is accomplished through the following five Specific Aims:
Specific Aim 1. To mount aggressive programs of research in cutting-edge bioinformatic
technology to address major challenges and opportunities in Network Biology. In past years, we
introduced a series of innovative methods including network-based biomarkers and network-based
stratification of genomes. In the next support period, we will research approaches to represent and
analyze network architecture across conditions or times; a general engine for genotype-to-phenotype
prediction using network knowledge; and a platform to crowd-source construction of a gene ontology
based wholly on network data from the community. Research proceeds in frequent communication
with Driving Biomedical Projects, which provide data and applications from the labs of our close
collaborators.
Specific Aim 2. To catalyze phase transitions in how biological networks are represented and
used in biomedical research. We are well-positioned to catalyze change along three complementary
themes: I. Moving from static network data and models to networks that are differential or dynamic, II.
Moving from networks that are primarily descriptive to those that are predictive of a range of
phenotypes and behaviors, and III. Moving from flat networks (lists of pairwise interactions) to multi-
scale representations that capture the hierarchy of modules comprising a biological system and
reflected in its data.
Specific Aim 3. To establish and disseminate robust end-user software, databases and high-
performance computing infrastructure that enable network analysis and visualization methods for a
broad biomedical research community. We will further develop the popular Cytoscape desktop
application and App Store database of network analysis tools into a mature platform for network-based
research on the web and in the cloud, to be called the Cytoscape Cyberinfrastructure. We will grow the
collection of supported network tools distributed through nrnb.org. And we will update and expand the
current NRNB computing hardware to keep pace with the growing size of network datasets and
computing tasks.
Specific Aim 4. To engage with leading biomedical investigators in productive collaborations
uniquely enabled by NRNB methods and tools. Since our inception in 2010, the NRNB has maintained
an active and rolling portfolio of approximately 60-80 Collaborative and Service Projects. We will use
best practices learned during this initial period of support to continue to acquire, triage, and/or
complete collaborative projects, with an increasing focus on deploying new network methodologies
developed under the three major technology themes outlined in Aim 2.
Specific Aim 5. To train the current and next-generation of biomedical investigators in the
science of Network Biology and its applications in disease research. The NRNB runs a highly attended
annual network biology symposium, which starting in 2014 is planned jointly with DREAM and
ICSB/RECOMB. We run a broad collection of network biology workshops and training events,
including a very popular Google Summer Of Code program which over the past 4 years has recruited
over 50 mentors and 100 students around the world to work on network biology projects under
matching support from Google.

18. OVERALL - RESEARCH STRATEGY
The mission of the National Resource for Network Biology (NRNB; http://nrnb.org/) is to advance
the new science of Biological Networks by providing leading-edge bioinformatic methods, software and
infrastructure, and stimulating the scientific community with a portfolio of collaboration and training
opportunities. Biomedical research is increasingly dependent on knowledge of biological networks of
multiple types and scales, including gene, protein and drug interactions, cell-cell and cell-host
communication, and vast social networks. NRNB technologies enable researchers to assemble these
networks and to use them to understand and predict how biological systems function and fail.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010;
the present application is a competitive renewal. It supports six major components, including:
§ Technology Research and Development of new bioinformatic methods and tools (3 TRDs)
§ Driving Biomedical Projects closely integrated with research and development (10 DBPs)
§ Collaboration and Service Projects within the greater scientific community (70+ CSPs)
§ Software and hardware infrastructure for Network Biology
§ Dissemination of methods, leading research, bioinformatic tools and databases
§ Training via symposia, workshops and a summer code development program
Technology Research and Development of new bioinformatic resources is the central activity of the
NRNB. As a significant deliverable, mature bioinformatic methods developed by each of the three TRD
projects are disseminated as freely available open-source software, many as apps to the Cytoscape
platform, a popular network analysis package that our team initiated in 2001. Each research and
development area is stimulated by an array of collaborators who are generating or using biological
network data within Driving Biomedical Projects. Specific DBPs are chosen to ensure that our
technology development is actively guided according to the needs of the scientific community. Over
time, the selection of active DBPs, and the more distal CSPs, is revised and expanded in consultation
with our External Advisory Committee. These research efforts are solidified by substantial software
and high-performance hardware infrastructure we have put in place over the past several years, along
with a broad program of training and dissemination.
During the previous funding period, NRNB investigators introduced a series of innovative methods for
network biology including network-based biomarkers1,2
, network-based stratification of genomes3,4
, and
automated inference of gene ontologies using network data5-8
. We put in place major software and
hardware infrastructure such as the Cytoscape App Store9
, an online market place of network analysis
tools, cBioPortal10
, a powerful web portal for analyzing cancer mutations in a network and pathway
context, and a high-performance computing cluster for network analysis. We established a large
portfolio of research collaborations (75 currently active); two annual international network biology
meetings, the Network Biology SIG and the Cytoscape Symposium; and a rich set of training
opportunities and published network analysis protocols. All of these efforts leveraged and further
supported our popular Cytoscape network analysis infrastructure.
Technology Themes
As a result of these efforts, we are nearing completion of a vision whereby a coordinated suite of tools
is able to gather public data on biological networks—including molecular, genetic, functional, and/or
social interactions; to visually and computationally integrate these networks with genomics and
proteomics data sets; to semi-automatically assemble all of the data into models of biological
pathways; to suggest points for experimental validation or clinical intervention; and to publish network
models at various stages of refinement online. Although work is still required to mature and iteratively
improve on this vision, the stage is now set to critically evaluate the state of the science and to take on
a new set of challenges.

19. During the next funding period, we will seek to bring about a major phase transition in how biological
networks are represented and used. In consultation with our External Advisors, we have identified
three complementary themes which identify major barriers or areas of need in pushing forward the field
of Network Biology, and for which NRNB is well-positioned to catalyze major change:
A. Differential Networks. In this TRD area, we will develop a series of tools and methodologies for
conducting differential analyses of biological networks perturbed under multiple conditions. The novel
algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover
biological networks under specific biological perturbations. The software tools developed in this project
enable researchers to further predict, analyze, and visualize the effects of these perturbations and
alterations, while enabling researchers to aggregate additional information regarding the known roles
of the involved interactions and their participants.
B. From Descriptive to Predictive Networks. Genomics is mapping complex data about human biology
and promises major medical advances. However, the routine use of genomics data in medical
research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this
TRD theme, we will use network information to help organize, analyze and integrate these data into
models that can be used to make clinically relevant diagnoses and predictions about an individual.
C. Multi-scale Network Representations. Although networks have been very useful for representing
molecular interactions and mechanisms, network diagrams do not visually resemble the contents of
cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein
complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and
so on. In this technology research project, we will pursue methods that move Network Biology towards
such hierarchical, multi-scale views of cell structure and function.
Figure 1. Themes of network-
based research. The NRNB
competitive renewal application is
organized around three major
themes. Two themes advance
technology for systematic assembly
of network models that are dynamic
(Theme I) and multi-scale (Theme
III). These advances in network
representation support the
remaining theme of using such
network knowledge to enable
general biological and biomedical
predictions, such as translation of
genotype to phenotype (Theme II).

20. These three themes serve as the major organizing principles underlying each of the three TRD
projects, along with matching DBPs (Figure 1, Table 1). These themes are timely, as the recent
accumulation of molecular network data in mammals, and the concomitant growth of new sequencing
and other ‘omics technologies, create remarkable new opportunities for using network-based
approaches to understand disease states on a genome-scale in human individuals.
Computational Network Biology: An evolving discipline
Biomedical research is at a critical juncture in which vast amounts of information on molecules and
molecular interactions have been collected, but methods to analyze these data are still in their infancy.
Technologies for interaction mapping are advancing at a staggering pace. Experimental approaches
include yeast two-hybrid assays11-19
, affinity purification coupled to mass spectrometry20-29
, reverse
PCA30
, chromatin immunoprecipitation of transcription factor binding31-42
, chromatin conformation
capture of DNA-DNA interactions43-45
, synthetic-lethal and epistatic networks46-57
, expression
quantitative trait loci58-66
, and many others. Apart from the molecular level, biological networks of other
types are central to a range of biomedical disciplines. For example, vast social networks connecting
friends, co-workers, and family members are being charted to lend insight into how disease is
transmitted67-70
. Such networks are being powerfully integrated with data from systematic profiling
experiments, such as changes in mRNA or protein abundance71-73
, protein phosphorylation state74
and
metabolite concentrations75
quantified with mass spectrometry, and other advanced techniques.
This enormous collection of networks and profiles necessitates bioinformatic frameworks to integrate,
filter, visualize, and interpret the data. A wide range of techniques are being developed to analyze
network data to address specific problems with relevance to basic biology and human health:
⇒ Gene function prediction. Since many genes still have unknown functions, examining genes in a
network context can provide important functional clues. For example, a protein connected to a set
of proteins involved in the same biological process is likely to also function in that process76-78
.
Table 1: Matrix of technology development and matching biomedical projects. Matrix view of
Technology Research and Development project aims (rows) grouped by themes (colors) and Driving
Biomedical Projects (columns) also grouped by themes (alternating gray), which are labeled in bottom header
row. A matrix cell is filled with a color corresponding to the Technology Theme for every case where a
particular DBP is driving a particular TRD aim. Each Technology Theme is driven by multiple biomedical
projects; 1-6 DBPs are associated with each TRD aim. Similarly, each Driving Biology Project is matched to
1-4 TRD aims, often across themes.

21. ⇒ Detection of protein complexes and other modular structures. There is clear evidence in biological
networks for modularity and principles of higher-order organization79,80
. For example, molecular
interaction networks have been found to cluster into regions that represent complexes81
, and
statistically over-represented motifs, such as feedback loops, have been discovered82
some of
which have been thoroughly analyzed83,84
.
⇒ Network evolution. Increasingly, the study of evolution is moving to the network level. A number of
techniques have been developed that identify common network structures and organizational
principles that are conserved across species85-88
, including our PathBLAST89
and NetworkBLAST90
tools which align protein networks to identify conserved interaction paths and clusters.
⇒ Prediction and further specification of interactions. Statistically significant domain-domain
correlations in a protein interaction network suggest that certain domains (and domain pairs)
mediate protein binding, allowing prediction of new interactions and binding interfaces91,92
. Machine
learning has also been applied to predict protein-protein or genetic interactions through integration
of diverse types of evidence for interaction93,94
.
⇒ Identification of disease subnetworks. A recent and powerful trend is to identify neighborhoods of a
biological network (i.e., subnetworks) that are transcriptionally active in disease95-99
. Such
neighborhoods suggest key pathway components in disease progression and provide leads for
further study and potential new therapeutic targets.
⇒ Subnetwork-based diagnosis. It has become clear that subnetworks also provide a rich source of
biomarkers for disease classification. Several groups3,100-103
have demonstrated that mRNA profiles
can be integrated with protein networks to identify subnetwork biomarkers—i.e. subnetworks of
interconnected genes whose aggregate expression levels or nucleotide variants are predictive of
disease state.
⇒ Subnetwork-based gene association. Despite the growing number of gene-disease association
studies104-106
based on Single Nucleotide Polymorphism (SNP) and Copy Number Variation (CNV)
profiling, most human genetic variation remains uncharacterized, both in biological mechanism and
in impact on disease pathology. We anticipate that molecular networks will continue to provide a
powerful framework for mapping the common pathway mechanisms impacted by a collection of
genotypes, as several proofs-of-principle have been developed in this area3,107-114
.
Major Changes from Prior Period of Support
NRNB has progressed in important ways since the initial application was funded in 2010. Numerous
minor, and several major, changes to the organization and focus of the resource were prompted during
regular reviews by our very engaged External Advisory Committee (2011, 2012, and 2014 meetings).
Generally, the EAC has found that “scientific progress has been impressive… [as highlighted by] the
spectrum of recent publications” and that “the collaboration and outreach strategy developed by the
NRNB has been very effective to make available and accessible network biology methods to those
researchers less versed in it” (written EAC report, July 12, 2014). They also have made constructive
critiques which were taken seriously and catalyzed changes in trajectory. Below are highlights of
critiques summarized from the progression of EAC meetings, followed by our corresponding actions:
Year-to-year milestones should be tracked more carefully as each TRD aim progresses.
At the suggestion of the EAC, we now track each TRD aim along a progression of four stages: [1]
Identification of a resounding need, [2] Research leading to a prototype method or proof-of-principle,
[3] Transition to a robust end-user tool; [4] Dissemination and collaboration to a wide audience.
Focus not only on network visualization but on network-based analysis and decision making.
Exciting progress was made for using networks as diagnostic biomarkers under original TRD A1,3,115
.
This enabled us, in the renewal, to seed an entire Theme 2 (TRD 2) on ‘predictive’ networks.
Encourage increased citation of NRNB tools such as Cytoscape. Track classroom use of Cytoscape.

22. We refactored nrnb.org and cytoscape.org websites to encourage citation and to better capture both
citations and non-cited usage. We identified 32 courses worldwide using Cytoscape (2011 – 2014).
Better track Cytoscape plugins and do more to encourage developers to write them.
We rebranded plugins as ‘Apps’ and obtained an NRNB award supplement allowing development of
the Cytoscape App Store (apps.cytoscape.org) which has greatly stimulated App development and
use.
Transition away from paper counts and toward metrics of community enablement and integration.
We now track metrics such as number of NIH grants using NRNB tools (3800!), third-party software
development we enabled (>200 Cytoscape Apps in App Store), and number of Google and NRNB
Academy mentees (59). We routinely solicit qualitative ‘testimonials’, of which we have received close
to 100.
Connect to synergistic resources like GenomeSpace and IGV and enable network sharing on the Web.
Cytoscape is now connected to RBVI's Chimera, Sage Bionetworks’ Synapse, GenomeSpace (a CSP
which transitioned to independent funding from NHGRI, PI Jill Mesirov), IGV and the Cancer Gene
Encyclopedia. We can now export networks to these and other resources using cytoscape.js and d3.js.
Optimize existing TRDs and DBPs to focus the core NRNB strengths. Include new, more distal DBPs.
In the renewal, TRDs have been organized around three themes which capture major challenges in
Network Biology with significant potential impact. We have added key DBP sites including Sage
Bionetworks (Stephen Friend), Dana Farber (Marc Vidal), and Stanford (Mike Cherry). These changes
in TRDs and DBPs have also prompted changes to our key personnel: James Fowler, who formerly
ran a TRD related to social network analysis, is now positioned as a social networks DBP, which
allows general network analysis methodology in multiple TRDs to impact social networks as one
driving project alongside other biological network types. We have transitioned former TRD D, related to
gene regulatory network inference, into a DBP with collaborators from Sage/DREAM.
NRNB Leadership and Administration
NRNB is led through the coordinated efforts of a team of four pre-eminent faculty in Network Biology
and Bioinformatics: Dr. Trey Ideker (University of California San Diego), Dr. Alexander Pico (Gladstone
Institute / University of California San Francisco), Dr. Gary Bader (University of Toronto), and Dr. Chris
Sander (Memorial Sloan Kettering Cancer Center). These investigators are well-known thought
leaders in the field with strong track records of innovative methods development and collaboration in
network biology tools and applications. NRNB administration is based at the University of California
with Dr. Ideker as Director and Alexander Pico as Executive Co-Director. Ideker and Pico together
coordinate activities among host institutions under a Consortium/Contractual arrangement with UCSD.
The four leaders are distributed across major biomedical research sites spanning both U.S. coasts and
Canada. This arrangement positions NRNB as a truly national and international organization with
broad exposure to, and involvement in, a spectrum of Driving Biomedical Projects and opportunities for
Collaboration and Service. Because NRNB expertise is geographically distributed, this allows each
NRNB site to harness the best local talent as well as to stimulate and respond to collaborative projects
within their local research communities. This is an inversion of the usual arrangement for resource
centers, in which the center is geographically localized and reaches out to investigators located
elsewhere. In contrast, NRNB is already global and thus “thinks globally, acts locally.”
NRNB Director, Dr. Trey Ideker’s credentials stem from a career of pioneering work in systems biology
and a very strong track record of creating innovative and widely-used bioinformatic tools. Ideker
received bachelor’s and master’s degrees in computer science from MIT and a PhD in biomedical
sciences working with Dr. Lee Hood at the University of Washington. Since 2001 Ideker has led an
independent research laboratory first as a Whitehead Fellow then UCSD professor, under continuous
NIH funding. Ideker’s research is led by the vision that, given the right experimentation and analysis, it

23. will be possible to automatically assemble maps of pathways just as we now assemble maps of
genomes. During graduate work, he developed a general iterative framework for how biological
systems can be systematically perturbed, interrogated and modeled. This framework laid the
foundation for many studies in the future discipline of Systems Biology116,117
. Using his new Cytoscape
platform, he demonstrated that biological networks could be integrated with gene expression to
systematically map pathways118
and aligned, like sequences, to reveal conserved and divergent
functions89,119-123
. He showed that the best biomarkers of disease are typically not single proteins but
aggregates of proteins in networks100
. Ideker was named a Top Ten Innovator of 2006 by Tech.
Review and received the 2009 Overton Prize, the highest award in computational biology. He serves
on the Editorial Boards of Cell, Cell Reports, Molecular Systems Biology, and PLoS Comp. Bio.
Directing NRNB builds on previous successful leadership such as Director of the San Diego Center for
Systems Biology, Genome Program Leader at the UCSD Cancer Center, former Division Chief of
Genetics in the Dept. of Medicine, and as a regular organizer of major conferences such as Cold
Spring Harbor Laboratory Systems Biology: Networks and the annual Protein Networks Track at ISMB.
NRNB Executive Director, Dr. Alexander Pico is the Associate Director of Bioinformatics at the
Gladstone Institutes on the UCSF Mission Bay Campus where he leads a Systems Biology Group.
After obtaining a PhD in molecular neurobiology and biophysics from Rockefeller University in 2003,
Dr. Pico joined Gladstone as a postdoctoral fellow in the laboratory of Dr. Bruce Conklin. Dr. Pico
develops software tools and resources that help analyze, visualize and explore biomedical data in the
context of biomolecular networks. He cofounded WikiPathways in 2007 and has contributed to the
development of Cytoscape since 2006. Dr. Pico received the David Rockefeller Award in 2001, the
Howard Hughes Medical Institute Predoctoral Fellowship from 1999-2003, the David and the Mary
Phillips Distinguished Postdoctoral Fellowship for 2005-2006.
NRNB Investigator, Dr. Gary Bader works on biological network analysis and pathway information
resources as an Associate Professor at The Donnelly Centre at the University of Toronto. He helped
found the field of network biology with early contributions to network visualization and modeling124-127
,
database resources128
and analysis81
. He was included twice (for Biology/Biochemistry and Computer
Science) on the Thomson Reuters list of most influential scientists worldwide in 2014
(www.highlycited.com). His research focuses on identifying causal pathways in disease and in
precision medicine (predicting patient outcome based on genetic, molecular, pathway and clinical
data). For example, using his methods, his team identified novel pathways involved in autism spectrum
disorder129
and the first potential therapy for a devastating pediatric brain cancer130
. He completed
post-doctoral work in Chris Sander’s group in the Computational Biology Center at Memorial Sloan-
Kettering Cancer Center in New York. Gary developed the Biomolecular Interaction Network Database
(BIND) during his Ph.D. in the lab of Christopher Hogue in the Department of Biochemistry at the
University of Toronto and the Samuel Lunenfeld Research Institute at Mount Sinai Hospital in Toronto.
He completed a B.Sc. in Biochemistry at McGill University in Montreal.
NRNB Investigator, Dr. Chris Sander is Chair of the Computational Biology Program at the Memorial
Sloan–Kettering Cancer Center in New York City. Sander was initially trained as a physicist, with a first
degree in physics and mathematics from the University of Berlin in 1967, and a PhD in theoretical
physics after graduate studies at the State University of New York, the Niels Bohr Institute in
Copenhagen and the University of California in Berkeley. Sander has made many contributions to
structural bioinformatics including developing tools such as FSSP and DSSP. DSSP, Sander’s
foundational algorithm for analyzing protein structures, is still used 17 years later as a standard for
identifying protein secondary structures in experimental 3D structures. In 1986, he became department
chair and founder of the Biocomputing Program at the European Molecular Biology laboratories
(EMBL) in Heidelberg with the support of Sydney Brenner. He was a founding member of the
International Society for Computational Biology (ISCB) when the society was established in early 1997,
and he is a former Executive Editor for the journal Bioinformatics. In 2010 he was awarded the ISCB
Accomplishment by a Senior Scientist Award.

24. External Advisory Committee. The above leadership team is co-advised by an External Advisory
Committee (EAC) which has historically been very active. The EAC consists of:
Dr. Stephen Friend, Founder and President of Sage Bionetworks (EAC Chair)
Dr. David Hill, Associate Director of the Harvard Center for Cancer Systems Biology
Dr. Tamara Munzner, Professor of Computer Science at University of British Columbia
Dr. Nik Schork, Director of Human Biology, J. Craig Venter Institute
Dr. Gustavo Stolovitzky, Systems Biology Group Leader at IBM/Watson Research Center
Dr. Marian Walhout, Professor of Molecular Medicine at University of Massachusetts
Dr. Ravichandran Veerasamy, NIGMS Program Representative
Each of these members brings relevant, world-renowned expertise and leadership in functional
genomics (Friend), protein networks and interactomics (Walhout, Hill), human genetics (Schork),
information visualization (Munzner), and inference of signaling and transcriptional regulatory networks
(Stolovitzky founded the well-known DREAM competition). The EAC met with NRNB leadership and
investigators to review progress at three separate face-to-face meetings during the past funding
period. The EAC also plays a critical role in the continual evolution of DBPs and CSPs, by suggesting
new collaborative opportunities in the field and approving all substantive changes to this lineup.
Projects are evaluated based on their ability to yield joint publications between resource personnel and
their collaborators, with a strong preference towards DBPs that are already NIH funded. For the DBPs
described in this proposal, all are already supported separately by the NIH or Genome Canada.
Size of the Research Community to be Served by NRNB
The community of scientists who might be considered “network biologists” has grown rapidly over the
past few years. As an illustration of how pervasive networks have become, the U.S. National Institutes
of Health currently funds 19,759 active research grants covering the topic “protein protein interactions”
which, remarkably, is up from 3,076 at the time of the last NRNB submission four years ago in fall
2010. Network research is also actively supported by a variety of initiatives from other major national
and supra-national funding agencies, such as the NSF, DOE, EU, and private foundations. As of 2014,
approximately one-third of papers presented at the major Bioinformatics conferences (e.g., ISMB,
RECOMB, ECCB) have focused on novel methodologies related to analysis or inference of biological
networks— this number is up from less than one tenth a decade ago. Network Biology is the focus of
major conferences per year, recently including the “Systems Biology: Networks” meeting (March 13-16
2013, CSHL); “Computational analysis of protein-protein interactions” (Sept 29 - Oct 3 2014; organized
by EMBO), and “NetBio” (July 1 -15 2014, organized by NRNB staff at ISMB).
Another indicator of the growing community of network research is usage of software tools. For
instance, use of Cytoscape has increased at a rate of ~50% per year since 2003. In the past year
alone, Cytoscape has been downloaded ~90,000 times and the website has had 370,000 sessions
and 880,000 pageviews. The NRNB-built and maintained App Store has delivered over 170,000 apps
to Cytoscape users since its launch in mid-2012. The original Cytoscape paper131
and the more
recently released update papers131-133
have together been cited over 12,000 times and used un-cited in
many more. Cytoscape analyses appeared in ~50 Science/Nature/Cell articles in 2013. Figure 2
shows how a subset of these works has been supported by a collection of different NIH institutes as
well as other funding agencies. These increases in software usage and development closely parallel
the increases in availability of large-scale network information and its potential impacts in biomedicine.

25. Thus, the importance of Network Biology is very much appreciated by basic scientists and clinicians,
many of whom use interaction databases and network analysis tools in their day-to-day research. On
the other hand, many other investigators (basic, clinical, and pharmaceutical) as of yet have little
knowledge of recent developments in network biology and would benefit greatly from outreach and
education centered on network-based technologies. With continued support of the NRNB National
Resource— focused on technology development supporting research, collaboration, education, and
dissemination— the Network Biology community could potentially become a great deal larger.
Progress Report Highlights
The initial period of support (Sept 2010 - Aug 2015) has led to the establishment of a Network Biology
resource which provides a unique and fertile environment for bioinformatics research, tool
development, dissemination, collaboration and training. The initial years have been very productive by
both quantitative metrics (cumulative) and research narrative:
Total publications: 83 total, 16 in Nature / Science / Cell family; 63 collaborative with ≥ 2 faculty
Cytoscape gets 8,000 dls/mo, 500 cites/yr and has supported 3,800 NIH grants
Number of network analysis Apps in Cytoscape App Store: 210
149 collaborations with external investigators on diverse topics (75 active)
92 training events in 12 countries, including 15 course lectures by NRNB staff
70 Google Summer of Code projects = $304,000 invested by Google
Workshops: 39 total, ~1,000 attendees
Symposia: 7 total, ~700 attendees
The below narrative highlights major progress under each component of the resource, including new
technology development, infrastructure, collaborative activities, dissemination, and training:
New Technology: Network Approach to Building Gene Ontologies. NeXO (The Network Extracted
Ontology) uses a principled computational approach which integrates evidence from hundreds of
thousands of individual gene and protein interactions to construct a complete hierarchy of cellular
components and processes5,7
. This data-derived ontology aligns with known biological machinery in
the GO Database and also uncovers many new structures (Figure 3).
NIGMS, 1005
NCI, 459
NCRR, 275
NIAID, 260NIDDK, 210
NHLBI, 202
NHGRI, 164
NIEHS, 157
NLM, 117
NIMH, 97
Wellcome Trust, 95
NINDS, 89
Canadian Institutes of
Health Research, 85
NIDA, 72
Others, 541
Figure 2: Use of Cytoscape
by grantees of funding
agencies. We have been
able to identify at least 3,800
active grants that depend on
Cytoscape, up from ~1,500
18 months ago. Information
was extracted from the
Acknowledgments section of
journal articles. One pie slice
represents the percent of
this total support provided by
a particular agency. Over
90% of grants are by NIH
institutes.

26. New Technology: Network-based stratification of tumor
mutations. NBS is a method for stratification (clustering) of
patients in a cancer cohort based on somatic mutations and
a gene interaction network3
. The method uses network
propagation to integrate genome wide somatic mutation
profiles for each patient over a gene interaction network,
and a non-negative matrix factorization based clustering
approach in order to derive biologically meaningful
stratification of a patient cohort.
Key Infrastructure: Cytoscape. Our team has worked very
closely together since 2002 to develop Cytoscape, a
standard visualization, integration and analysis package for
biological networks (http://www.cytoscape.org/). Our
previous Cytoscape development efforts, which predate
NRNB, demonstrate the strong commitment of our
leadership team to open-source bioinformatic tools. Our
ability to establish a strong resource center owes in large
part to the groundwork we have put in place over the
previous decade of Cytoscape development. During the
funding period, NRNB helped expand the Cytoscape
desktop application into a suite of related tools, Apps,
websites and databases which we propose to call the
Cytoscape Cyberinfrastructure.
Key Infrastructure: Establishment of the NRNB Compute Cluster. Over the performance period we
worked closely with the San Diego Supercomputer Center (SDSC) to establish a high-performance
computing environment for network biology applications. Through NRNB funds and matching SDSC
support, we constructed an advanced cluster centered on 512 cores (64GB and 256GB HP SL230s
Gen8 blade nodes) with 128TB attached storage. This infrastructure has been highly used and critical
for new technology development, as manipulation, search, and statistical scoring of large network
structures is typically compute intensive. We propose to renovate and expand this infrastructure in the
next funding period.
Dissemination: The Cytoscape App Store. A highlight of NRNB Dissemination efforts is the Cytoscape
App Store (http://apps.cytoscape.org/) which was developed under supplemental funding to the main
NRNB award. The goals of the App Store are to highlight the important features that apps add to
Cytoscape, to enable researchers to find apps they need, and for developers to promote their apps. It
has stimulated a sizable community of Cytoscape App developers, enabling over 200 third-party
network analysis and visualization tools to-date.
Dissemination: The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for
interactive exploration of multi-dimensional cancer genomics data sets, currently providing access to
data from more than 5,000 tumor samples from 20 cancer studies10
. The cBio Cancer Genomics Portal
empowers researchers to translate these rich data sets into biologic insights and clinical applications,
for instance by viewing the molecular network context of cancer mutations using cytoscape.js for the
web. Following its introduction in 2012, the portal has been used by thousands of basic cancer
researchers, and increasingly clinicians, wishing to understand the prevalence, biological context, and
outcome of a set of tumor mutations.
Key DBP Collaboration: Synthetic Genetic Analysis of Budding Yeast. NRNB investigator Gary Bader
had a very productive DBP with scientists at the University of Toronto (Charles Boone, Brenda
Andrews) leading to the first complete genetic interaction network for a cell48,134
. This reference map
provides a model for expanding genetic network analysis to higher organisms and is stimulating
Figure 3. Journal cover showing top level
view of the Network-Extracted Ontology
(NeXO), a completely data-driven
reconstruction of the literature-based GO.

27. Figure 5. Differential genetic interactions specifically
highlight pathways responding to DNA damage. Genetic
interaction module map in which protein complexes / pathways
(nodes) are connected by differential genetic interactions
(edges). “Differential positive” scores indicate genetic interactions
that increase after exposure to MMS (green) while “differential
negative” scores indicate interactions that decrease (red).
valuable insights into gene function and
drug targets. The 2010 publication of
roughly 20% of the complete map was
in the top 30 most cited papers of that
year (Figure 4).
Key CSP Collaboration: Genetic
Networks Remodeled by DNA
Damage. Although cellular behaviors
are dynamic, the networks that govern
these behaviors have been mapped
primarily as static snapshots. To
explore network dynamics, Ideker
collaborated with Nevan Krogan at
UCSF to develop an approach called
differential epistasis mapping, which
creates a genetic network (Figure 4)
based on changes in interaction
observed between two static
conditions. They mapped widespread
changes in genetic interaction as the
cell responds to DNA damage57
. Differential networks chart a new type of genetic landscape that will
be invaluable for mapping many different cellular responses to stimuli.
Training: Google Summer of Code. One of our most successful training activities is our participation as
a mentoring organization in the Google Summer of Code program. Over the past 4 years, we have
recruited over 50 mentors and attracted over 100 students around the world to apply to work on
network biology projects. For example, Google funded 18 students recruited by NRNB in 2014, ranking
us in the top 10% in terms of the number of slots allocated by Google across all accepted
organizations (comparable interest to Apache, Mozilla, R, and Python). Our overall application success
rate is 94% on GSoC projects. These
training efforts have resulted in 10
student co-authored papers9,135-143
.
Training: RECOMB/DREAM Conference
and Challenges. Another highlight of our
training efforts has been to create an
annual international symposium on
bioinformatic methods for Network
Biology. Formerly this was the
Cytoscape Symposium, a three-day
annual meeting that attracted 200-300
attendees each year. In 2013 we began
discussions with the well-known
RECOMB (Research in Computational
Molecular Biology) and ISCB
(International Society for Computational
Biology) conference families to combine
efforts. These discussions led to an
agreement to expand the existing
RECOMB/ISCB conference series on
Systems and Regulatory Biology
(originally co-founded by Trey Ideker,
Ron Shamir, and Eleazar Eskin in 2003)
to integrate sessions including a
Figure 4. The genetic interaction map of budding yeast, as
visualized by a DBP between NRNB and the Boone laboratory.

28. Cytoscape App Expo and Cytoscape user and developer training events. At the same time, NRNB
partnered with the popular DREAM Challenge, an annual competition that nucleates thousands of
participants to address important problems in inference and validation of network biology models.
DREAM is also co-organized with the RECOMB conference and, since 2013, the winning network
inference method(s) have been implemented as Cytoscape Apps by the donated time of an NRNB
software developer.
Future Progress Assessment and Evaluation
NRNB progress will be measured using both narrative and numerical information. All investigators
provide annually a narrative of research progress and future plans within each of the TRDs and
coupled DBPs. We maintain a database of bibliographic data that tracks the number of peer-reviewed
publications per TRD and investigator. We track the number of citations to these papers along with
near-term metrics such as the ‘Altmetric’, which indicates rapid online interest in a paper even before
enough time has passed to garner follow-on citations.
Beyond publications, an important NRNB aim is production and maintenance of cutting-edge software
tools, websites, and platforms. These resources will be posted to our website in a manner consistent
with NIH policies (see Resource Sharing Plan), and we will track usage statistics of all new and
existing NRNB-supported resources (e.g. cytoscape.org, apps.cytoscape.org, nexontology.org). We
will also track tools developed by third parties, such as Cytoscape Apps, which are enabled or
supported by core NRNB efforts.
Success of Collaboration and Service Projects is measured by the overall number, complexity, and
quality of ongoing CSPs we manage and the publications stemming from these collaborations. We
also routinely solicit qualitative feedback about CSPs and tool usage in the form of user testimonials.
Products of training and outreach, such as workshops, symposia, and summer training programs like
Google Summer of Code, are tracked via event descriptions and post-event surveys and metrics such
as the number of events and attendees.
Distinction Between NRNB And Existing Resources
NRNB complements other bioinformatic resources and centers, while having unique aims and
deliverables. The following is a partial list of other NIH-supported bioinformatics efforts that are distinct,
but synergistic, with NRNB:
The Cytoscape Project. Cytoscape is a software platform on which to provide tools and infrastructure
for biological network analysis. The core codebase of the Cytoscape desktop application is supported
by a research project grant from NIGMS (R01 GM070743 to Ideker). In contrast, NRNB provides
training and outreach on Cytoscape and its apps, and TRD projects use the Cytoscape platform for
analysis and development of new tools as Apps. NRNB also supports a range of other tools and
databases such as Wikipathways, Enrichment Map, NeXO, the Cytoscape App Store, and
Cytoscape.js/web.
Resource for Biocomputing, Visualization, and Informatics (RBVI) directed by Thomas Ferrin at UCSF.
RBVI is another informatics BTRR that develops software for protein structural analysis and
visualization, including Chimera. This resource has already developed the StructureViz144
Chimera to
Cytoscape App, which links protein networks analyzed in Cytoscape to 3D structures of proteins145,146
.
National Resource for Cell Analysis and Modeling (NRCAM) directed by Leslie Loew at University of
Connecticut. The NRCAM is another informatics BTRR that develops the VCell modeling tool147
which
enables precise physico-chemical simulations of pathway dynamics given information on kinetic
parameters, cellular sub-structure, and localization. In contrast, the focus of NRNB is to integrate