Annual report for the P41 supporting the National Resource for Network Biology

1. Overall
B.2:
What
was
accomplished
Highlights
from
the
past
year
include:
• 118 publications citing NRNB grant
• Over 8000 visits per week to Cytoscape.org
• 17,000 downloads per month for Cytoscape
• 3700 Cytoscape application launches per day
• 38,261 page views in January 2017 for the Cytoscape App Store, and an average of 875 downloads per
day among 307 apps.
• A total of 18 tools supported by NRNB
• 93 new and ongoing collaborations with external investigators on diverse topics
• 3 students trained at NRNB Academy
• 15 students trained through Google Summer of Code
• 16 NRNB coordinated training events in 10 locations in 6 countries
• Over 100 users and dozens of developers trained on Cytoscape by NRNB staff
• 66,000 unique sessions at Open Tutorials in the past year, 65% from new visitors
Technology
Research
and
Development
Progress on the first theme of Differential Networks includes work on an improved perturbation biology method
applied to the modeling of time resolved drug response measurements in melanoma cells. The temporal
response to CDK4 inhibition in liposarcoma is a driving biology project (DBP 7 with Forest White) we are
investigating in terms of signaling networks. We also continued to develop protein-protein interaction network
alignment algorithms in support of DBP2 with Drs. Marc Vidal and David Hill. And we implemented new tools in
Cytoscape for working with mass spec data to facilitate future differential network analysis. This work was
shared with our DBP 1, the Krogan lab, from which we continue to collect valuable end-user input to design
and prioritize our tool development.
The second theme of Descriptive to Predictive Networks saw progress on two specific sub-aims. We
developed a supervised patient classification framework, called netDx, that uses patient similarity networks in a
generalizable and integrative manner. In support of DBP 5 with Sage Bionetworks, we reanalyzed all available
DREAM challenge datasets. In terms of biomarker identification for cancer progression and treatment
response, we are developing the platform for data preparation and prototyping regression analyses that will
inform future network-constrained regression analysis, such as GELnet. The application of this technology to
drug response prediction for the NCI compound library is part of our DBP 8 with Dr. Pommier. Finally, we
developed a supervised machine learning model, called DeepCell, that uses the hierarchical structure of the
cell and heat diffusion modeled signaling to simulate cell behavior. Using both the Gene Ontology and a data-
driven ontology, DeepCell can accurately predict phenotypes including both growth rate and genetic interaction
score across a range of genetic interaction scores.
Progress on the third theme of Multi-scale Networks includes the development of a general progressive
procedure, Active Interaction Mapping, which was used to assemble a comprehensive ontology of functions for
autophagy. This work continues to be motivated by the data and prediction challenges in DBP 3 and 4, Mike
Cherry (GO) and TCGA projects. We have also begun experimenting with using single cell RNA-seq data to
improve the resolution of inferred cell-cell interaction networks. These are being applied to cancer stem cell
biology and regenerative medicine. This work is being driven by Dr. Zandstra’s sustained interest in both inter-
cellular networks and cell fate regulation, DBP 9.

2. NRNB
Workgroups
In addition to our TRD projects, we launched two new initiatives to foster greater interaction and collaboration
across NRNB sites and to track opportunities in developing research areas relevant to network biology. We are
calling these NRNB Workgroups.
The first workgroup is focused on Single Cell Genomics and Stem Cell Research. NRNB staff from the
Pico, Morris and Bader groups are meeting quarterly to coordinate on tools, datasets and pilot projects in this
area. Each group has collaborators in this area that may develop into CSPs or even DBPs for future TRD
projects. In addition to collecting resources and comparing notes, we have identified two specific pilot projects
to work on as an NRNB team: cell-cell interactions networks and improvements over t-SNE plots for data
analysis and visualization. The former will involve curating pathways that are transcriptionally active
downstream from receptors involved in cell-cell communication, followed by pathway analysis on single cell
datasets. The latter will involve exploring alternative clustering algorithms, such as SIMLR, and performing a
comparative assessment using Cytoscape protocols.
The second workgroup is focused on Patient Similarity Networks (PSNs). Within the scope of technology
development, both NDEx (Ideker) and cBioPortal (Sander) will be leveraged in this project. The Bader group is
currently working on depositing PSNs into NDEx with sufficient metadata and filter options. The Pico group is
also working on modeling WikiPathways content for deposition into both NDEx and Pathway Commons. These
efforts represent improved interoperability among NRNB resources. Data visualization at cBioPortal and import
into Cytoscape are also being explored in order to facilitate analysis, for example using Network Based
Stratification methods described in prior NRNB reports, and integrated data views.
We will continue to lead the coordinated efforts of these workgroups during the next reporting period and
spawn new workgroups as new data types and opportunities are presented.
Collaboration
and
Service
Projects
NRNB staff have initiated 18 new collaboration and service projects over the reporting period, for a total of 93
collaborations maintained or completed over the last year. A summary table is provided in the CSP component
report, along with summaries of major project from each of the four sites led by the co-PIs. In broad strokes,
the projects span patient similarity network methods development applied to various cancers, cell-cell
interaction network analysis applied to Head and Neck cancer, EnrichmentMap network analysis on single cell
RNA-Seq data applied to colorectal cancer, as well as standard network and pathways analysis of stimulated T
cells, neuroinflammation, glutathione metabolism, microgravity effects, drug transporters, Multiple Sclerosis
and Huntington’s Disease. In addition to service-based collaborations, we managed the code development
collaborations between ~45 mentors and ~18 students during this reporting period through the Google
Summer of Code and NRNB Academy programs, combined.
Infrastructure
In our 2016 report, we described the creation of initial technologies needed to create an ecosystem of
biologically valuable Internet-based services that exchange network data in a stable, performant, scalable,
reusable, recombinable and reliable manner. In 2016, we created and released Diffusion, which is the first
Cytoscape app to demonstrate the integration of basic CI service technologies (e.g., Cytoscap apps, CX,
request routing and service deployment). The Diffusion app calls the CI’s new Diffusion service, which uses a
heat propagation approach to identify subnetworks worthy of focused study, given a list of nodes in a large
network. The Diffusion service demonstrates how the CI can allow typical biological programmers to
dramatically increase the audience for their code and gain access to Internet-scale computational resources.
The CI framework on which Diffusion is built leverages modern Kubernetes cluster technology (to augment
Elsa, see section C.3), common CX-based message formats, server-based interface stubs, call metering and
central logging to enable biological programmers to package algorithmic code as a highly scalable, highly
available microservice with access to server- and cluster-class computing resources. We also launched
cyREST2 as a significant expansion of the highly successful cyREST Cytoscape feature
(http://apps.cytoscape.org/apps/cyrest). Critically, cyREST2 will enable access to functionality available
through Cytoscape apps, including enrichment, clustering, network acquisition, enhanced graphics and graph
analysis. We will work with both the Python/Jupyter and R communities to upgrade their cyREST interface
support (e.g., http://bioconductor.org/packages/release/bioc/html/RCy3.html). And we created the deep-cell

3. web app and service in support the Deep Cell phenotype prediction research described in the complete
Infrastructure report. Finally, both the Cytoscape App Store and NRNB Compute Cluster continue to thrive and
serve as major NRNB infrastructure components.
Dissemination
NRNB.org is the main web site for the National Resource for Network Biology and serves as the primary
source of disseminating NRNB resources and associated information. It is constantly updated with information
for NRNB collaborators and researchers as well as the larger network biology community. The site includes our
project description and annual reports, available tools and resources, links to training materials, programs and
events, and instruction in how to collaborate. The attentive maintenance and updating of the site helps make
the #2 Google search result for "network biology tools", second only to Cytoscape.org, an NRNB supported
tool. NRNB.org is the #3 result even when searching for just "network biology". These are global, non-
personalized results. Over the past year, traffic to the site averages about 840 visits per month. Since the site
went live in late 2010, we have had over 84,000 visits.
Since our last report (March 2016), we significantly improved our discourse on Cytoscape history and
future directions: http://www.cytoscape.org/roadmap.html. It now more plainly lays out our vision for Themes
and Features for future releases, and is laid out to improve communication with users, developers and curious
parties. Since January 2016, monthly downloads have grown to an average of 17,000 per month for the year,
with Cytoscape v3 accounting for the vast majority of downloads. Extensive graphs and descriptions of
Cytoscape usage are provided in the Dissemination report.
During this period, the Cytoscape App Store, which was created as an NRNB supplement project,
continues to serve as the major source of dissemination for Cytoscape apps and related documentation. The
App Store hosts over 307 apps developed by 674 different developers around the world. Cytoscape users
download an average of 850 apps per day over the past 12 months. That has accumulated to just over
760,000 total app downloads since the launch of the App Store. The top 3 downloaded apps, ClueGO, BiNGO
and GeneMANIA, have accumulated over 136,000 downloads combined. During the month of January 2017,
the site received over 38,000 page views. Graphs of app submissions, site visits and referral sources are all
provided in the Dissemination report.
NRNB staff members are responsible for maintaining these additional sources of dissemination:
• Three Cytoscape mailing lists: helpdesk, app-dev and cytostaff
• Open Tutorials: http://opentutorials.cgl.ucsf.edu/index.php/Main_Page
• Cytoscape Publications Tumblr: http://cytoscape-publications.tumblr.com/
• Network Biology Publications Tumblr: http://netbiopub.tumblr.com/
• LinkedIn Network Biology Group: https://www.linkedin.com/groups/5123610
• F1000Research Cytoscape App Channel:
http://f1000research.com/channels/cytoscapeapps
• GSoC and NRNB Academy: http://www.nrnb.org/gsoc.html
Training
In addition to the global training support provided by our Training Coordinator, Dr. Morris, we also leverage the
fact that we are a multi-site resource and are thus able to host local training events on multiple campuses. We
also provide materials, training and advertising for events presented by non-NRNB staff. The Training report
includes a table of 16 events coordinated by the NRNB, including courses, workshops, clubs and lectures in 10
locations in 6 countries.
After taking a year off from Google Summer of Code (GSoC) in 2015, and instead running our own summer
training program (NRNB Academy Summer Session), we gathered over 50 project ideas and close to 40
mentors for GSoC 2016. We were accepted as a mentoring and had one of our most successful years yet, with
all 15 enrolled students completing their projects. New for this year was also the development of a Mentor
Resource Packet, a collection of resources designed to help mentors with recruiting students. In addition to the

4. technical accomplishments and productivity of our students, we are also proud of the many important aspects
of diversity our students represent in the GSoC program, including geographical, gender and academic. A few
statistics of our diversity is listed in the below table, with overall GSoC numbers in parenthesis:
• 9 different countries represented, including 1 (of 2) from Croatia, 1 (of 3) from Armenia and 2 (of 12)
from Turkey
• 20% female (compared to 12% overall)
• Only 67% Computer Science (compared to 78% overall), we included PhD students in Biological
Oceanography and Medical Biochemistry & Biotechnology, an MS student in Bioinformatics, and a pre-
med undergraduate.
Our complete 2016 end-of-year report can be found here: http://nrnb.org/gsoc-reports.html. We have
received and abundance of testimonials from students and mentors, a subset of which are available on our
website: http://nrnb.org/testimonials.html#collab-tab.
B.4:
What
training
opportunities
The collaborations during this period included many requests to prepare a custom training events and one-on-
one sessions. During this reporting period, the Pico, Bader and Ideker groups offered support to local
researchers via consulting meetings and one-on-one training sessions with the aim for biologists to learn how
to use NRNB tools in their research. For example, how to install Cytoscape on personal computers and
navigate through a network as well as training on how to go through the Bader lab enrichment analysis
standard pipeline which can be summarized under these steps: 1) run GSEA or g:Profiler or similar gene-set
enrichment tools 2) create a network of enriched pathways using Cytoscape/EnrichmentMap 3) perform post-
analysis using EnrichmentMap features or GeneMANIA. Additionally, 15 of the collaboration projects listed in
B.2 served as intensive training opportunities for students accepted into our NRNB Google Summer of Code
program. The students learned not only about NRNB tool development, but also about open source software
development with a distributed team.
Our Training effort leveraged the fact that we are a multi-site resource and are thus able to host local
training events on 4 different campuses. We also provided materials, training and advertising for events
presented by non-NRNB staff. Each year we provide 100’s of researchers an introduction to network biology
concepts and Cytoscape usage. We also train dozens of programmers how to write apps for Cytoscape to
provide domain-specific functionality to the platform. These programs have been very successful so far. This
is evident from the testimonials we collect via survey following each event:
http://nrnb.org/testimonials.html#collab-tab. Here are snippets from this year’s students and mentors in our
Google Summer of Code and NRNB Academy programs:
“The NRNB program is a fantastic opportunity to gain skills and work experience in network
biology and app development, at any stage in your academic career. I came in as a
graduate student with only a few months of coding experience and now I've released my first
application. Exhilarating!”
“It has helped improve the software developed by my group. It has also given me
experience in mentoring someone long distance.”
“Working in an NRNB training program helped to strengthen my resume and introduced me
to the idea of combining a career in medicine with computer-based research.”
“Great opportunity for developing mentoring and supervising skills as well as get my
software tools developed.”
“This was my first ever contribution to an open source project and NRNB also. This
milestone will shine on my CV forever.”

5. “Great experience interacting with the community and my mentor. I was excited to receive
help and encouragement for my project.”
“Learned how to work in a collaboration, formulate better questions. Gained especially
invaluable knowledge and experience. Improved coding skills. Learned new programs and
libraries.”
“It broadened my mind to issues still unsolved in the network biology community, and I
gained resources and colleagues in the community that I otherwise wouldn't have.”
“Personally, I see great value in interacting with smart, young people from all around the
world. I am optimistic that participating in NRNB training programs will benefit my own
research group by giving it wider exposure and by building a community around the
software.”
“I am continuing to work for Cytoscape.js and am happy to being staying involved.”
“The program has been great experience for my students. They not only learned about open
source community driven projects, but the work they did has contributed to their future
research.”
“The program gave me a chance to work with students in projects of mutual interest and to
develop my tools faster and more efficient.”
B.5:
How
have
results
been
disseminated
Technology
Research
and
Development
Technology research and development results are routinely published (see C.1) and discrete software tools
and resources are highlighted and distributed through the NRNB web site at http://www.nrnb.org/tools-
wall.html. We also created and maintain almost 100 open source code repositories for NRNB related projects
at GitHub, https://github.com/nrnb/.
Infrastructure
We routinely promote Cytoscape and other NRNB infrastructure advancements through publications and via
the tools page on the nrnb.org web site. Publications citing Cytoscape continue to increase year over year,
numbering 2218 in 2016, a 24% increase over 2015. NRNB staff were involved in at least 13 publications using
Cytoscape and results obtained on the NRNB cluster. These are listed in the Infrastructure report.
B.6:
What
you
plan
to
do
next
Technology
Research
and
Development
For the first theme of Differential Networks, we aim to modify the perturbation biology modeling method to
incorporate time resolved data. With respect to DBP 7, with the sample collection and profiling complete, we
will next focus on data analysis. We will also continue the work on developing an evolutionary model that
considers domain and binding site changes and their affects on network alignment. Finally, we will extend the
integrated ID mapping tool in Cytoscape to handle general annotation tasks and begin work on a gene set
manager. Both of these tools will be in support of core protocols, including the mass spec analysis workflows of
DBP 1.
Work on the second theme of Descriptive to Predictive Networks will extend netDx to other disease areas
and other features types, including epigenomics and non-coding genome regions. We will continue the
development of network-constrained regression models and apply them to NCI compound screen datasets.

6. And we will study the predictive process of DeepCell to glean insights into the functional logic underlying a
particular genotype-to-phenotype response.
The third theme of Multi-scale Networks will see the addition of pharmacological and clinical datasets to the
data-driven assembly of ontologies of drugs and phenotypes. In the next reporting period, we will integrate
cancer ‘omics data into HNeXO to make a cancer-specific gene ontology. And, finally, we will continue to
improve our network inference using cell-cell receptor-ligand pathways and its ability to leverage single cell
RNA-seq data.
Collaboration
and
Service
Projects
New CSP requests are coming in all the time. We will continue to evaluate these per site as we have. This
includes the approach being tested by Gladstone and UCSD sites to have their respective Bioinformatics core
facilities explicitly offer NRNB services as part of their regularly advertised campus services. Both groups are
seeing many projects funnel in through this mechanism. We will continue to evaluate this approach and scale
it where appropriate. See the CSP report for a more detailed description of specific projects on the horizon at
each site.
Infrastructure
The overall goals for the Cytoscape Desktop are published on the Cytoscape Roadmap web page
(http://cytoscape.org/roadmap.html). The Infrastructure report summarizes these and goes into detail on future
Cytoscape Cyberinfrastructure, App Store and NRNB Cluster work plans.
Training
We recently submitted our application for GSoC 2017. If accepted, this should be one of our largest years yet.
We have more mentors and more project ideas than prior years and are continuing a more coordinated
outreach effort with a Mentor Resource Packet that we will distribute to all NRNB mentors. This resource was
developed in 2016, and is meant to help mentors contact and communicate with various student bodies that
are likely to have the skill and interest to participate in GSoC 2017.
C.2:
Website(s)
or
other
Internet
site(s)
NRNB.org
NRNB.org is the main web site for the National Resource for Network Biology and serves as the primary
source of disseminating NRNB resources and associated information. It has information for NRNB
collaborators and researchers as well as the larger network biology community. The site includes our project
description and annual reports, available tools and resources, links to training materials, programs and events,
and instruction in how to collaborate. Over the past year, traffic to the site averages about 840 visits per month.
Since the site went live in late 2010, we have had over 84,000 visits.
Cytoscape.org
As detailed in the Dissemination report, we significantly improved our discourse on Cytoscape history and
future directions: http://www.cytoscape.org/roadmap.html. It now more plainly lays out our vision for Themes
and Features for future releases, and is laid out to improve communication with users, developers and curious
parties.
Visits to cytoscape.org now number almost 1.9M (up 26% from last year) since the site was created in
2012. While most visits to cytoscape.org are from the United States, these visits aren’t in the majority. In fact,
the second greatest source of visitors is “all the rest”, indicating that Cytoscape is popular worldwide.
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 and install apps they need,
and for developers to promote their apps. It has stimulated a sizable community of Cytoscape App developers,
hosting over 307 apps developed by 674 different developers around the world. Cytoscape users download an

7. average of 850 apps per day over the past 12 months. That has accumulated to just over 760,000 total app
downloads since the launch of the App Store. The top 3 downloaded apps, ClueGO, BiNGO and GeneMANIA,
have accumulated over 136,000 downloads combined. During the month of January 2017, the site received
over 38,000 page views.
OpenTutorials
Open Tutorials (http://opentutorials.cgl.ucsf.edu/index.php/Main_Page) is the main source for tutorial materials
for Cytoscape and other NRNB tools, and is being used both internally by presenters, and by researchers and
developers. Traffic to Open Tutorials is consistent, with 66,000 unique sessions at Open Tutorials in the past
year, 65% from new visitors.
Others
As detailed in Administrative, Dissemination and Training reports, we also maintain a handful of other sites
related to NRNB activities, including
• Network Biology LinkedIn group
• Tumblr feeds for Network Biology- and Cytoscape-related publications
• Special pages for GSoC and NRNB Academy
• Special pages for annual NetBio SIG conference
• Guest editor roles for F1000Research Channel for Cytoscape Apps
• New Cytoscape App Developer Ladder
• New site for hosting a dynamically generated manual for Cytoscape
• Three mailing lists for Cytoscape users, app developers and core staff
C.3:
New
technologies
and
techniques
TRD1.1
The developed perturbation biology methodology has been publicly shared via publication. Additionally, there
is an accompanying web application (http://www.sanderlab.org/pertbio/) that is available. Users can explore
and download models produced by the analysis.
TRD1.3
The new identifier mapping tool described in B.2. will be shared through the free, open source distribution of
Cytoscape 3.5.0+ as of March 2017. The stringApp is freely available as an open source app for Cytoscape at
http://apps.cytoscape.org/apps/stringapp. The app now includes STITCH support as described in B.2. The
stringApp has been downloaded over 7400 times since it’s release 13 months ago.
TRD2.1
We have developed the netDX technology and will disseminate it at the netdx.org website and GitHub, under
an open access software license. The technology is implemented in R and Java, as an easy to use and well-
documented R package.
TRD2.2
We anticipate that several useful resources will be generated by the proposed research. We will provide a new
deep learning training algorithm to train the hierarchy-guided deep neural network. We will provide a new
analysis pipeline to help people the behavior of their supervised machine learning model. We will also provide
a web server where users can not only predict growth related phenotypes using our trained deep learning
model but also interpret the logic of prediction.
TRD3.2
• An online, interactive viewer of the Active Interaction Mapping procedure and its application to yeast
autophagy at http://atgo.ucsd.edu.

8. • A data-driven gene ontology in human: We will also make this ontology available through an online,
interactive viewer.
• Parallelized ontology construction: We will make code available on GitHub once completed.
Infrastructure
technologies
Detailed in section C.3 of the Infrastructure report are the following technologies:
• CX network interchange format
• cyWidget system
• Kubernetes cluster
C.5.a:
Other
products
TRD
3.2
A significantly faster version of the popular random forests regression algorithm in the Python scikit-learn
package was created for this work and is publicly available on GitHub at https://github.com/michaelkyu/scikit-
learn-fasterRF.

9. TRD
1:
Differential
Networks
B.2
What
was
accomplished
under
these
goals?
TRD 1.1: Tools for Inference of Differential Networks from Protein States and Abundances Over Time; DBP 7:
Forest White; DBP 8: Pommier
Background: The aim of this task was to improve the perturbation biology method (developed by Nelander,
Molinelli, and Korkut) for a more thorough understanding of protein networks and their responses to drug
perturbations. The perturbation biology method involves inference of quantitative signaling models from high
throughput drug response data. In recent years, we solved the network inference problem through
implementation of a probabilistic statistical physics algorithm called belief propagation (BP). In network
inference, we also benefit from pathway database extracted prior information to improve model accuracy. The
network models are based on coupled nonlinear ordinary differential equations that represent the temporal
changes to perturbations.
Equation
1:
In Equation 1, xµ
i are the perturbed and/or measured variables, µ, represent the perturbations, wij quantifies
the edge strength, αi constant is the tendency of the system to return to the initial state, and εi constant defines
the dynamic range of each variable i. The transfer function, Φ ensures that each variable has a sigmoidal
temporal behavior.
Current progress: The drug response was previously measured and analyzed at a single time point. This is a
limitation since the drug response changes over time and early changes at the protein level might be of
importance for the understanding of the drug response. We have therefore produced and analyzed a new data
set with time resolved drug response measurements in melanoma cells. The data contains protein
measurements at several time points during 3 days (10, 27 minutes, 3, 9, 24, 48, 67 hours) as well as
phenotypic measurements (cell death and growth) for 60 different drug combinations in melanoma cells.
Recent analysis shows that early protein measurements may be important to explain cell death measurements.
1. We used partial least square regression (PLSR) modeling to find relations between protein
measurements and cell death at different time points. In PLSR, the input and output variables are
projected to new dimensions (components) to find a linear regression model. We used the protein data
at 8 time points with 60 drug combinations as input, and chose the number of components in the PLSR
so that 95% of the variance in the output (cell death) was explained. The resulting model with 5
components are in agreement with data as shown in Figure 1.
2. To evaluate the contribution of the proteins to the regression model, we used VIP (variable importance
of the prediction) scores. These scores are calculated from the variance that is explained by each
variable and the total variance that is explained by all components. VIP scores are always positive, but
since it is important to know the direction of the response, we used the sign from correlation between
the protein measurements and cell death. As seen in Figure 2, early time points for some of the
proteins have a high VIP score, which means that these measurements are important to be able to
explain the outcome.

10.
Figure
1.
The
PLSR
model
is
in
agreement
with
data.
The
PLSR
(partial
least
square
regression)
model
was
in
good
agreement
with
the
cell
death
measurements
(left).
The
number
of
components
in
the
PLSR
model
was
chosen
to
be
5
since
a
PLSR
model
with
5
components
explain
95%
of
the
variance
in
the
measurements
(right).
Figure
2.
VIP
scores
for
key
proteins
show
the
importance
of
early
protein
measurements
to
explain
cell
death.
The
VIP
(variables
importance
of
the
prediction)
scores
are
calculated
from
the
PLSR
model
using
the
variance
that
is
explained
by
each
model
variable
and
the
total
variance
that
is
explained
by
all
components
of
the
model.
The
measured
proteins
AKT-­‐
pS473
and
PRAS40-­‐pT246
(left
side)
are
important
to
explain
cell
death
already
at
74
minutes
after
drug
addition.
In service of our DBP 7, Temporal response to CDK4 inhibition in de-differentiated liposarcoma, we have
been investigating how signaling networks in two patient-derived xenograft models (DDLS8817-PDX;
MPNST3-PDX) respond to clinically-relevant inhibition of CDK4 and combinations designed to block potential
network resistance mechanisms. The PDXs are initially sensitive to CDK4 inhibition, but ultimately the tumors
begin to grow even in the presence of the drug. Proteomic profiling of peptides enriched for phoso-tyrosine
from treated and untreated animals revealed increases in key signaling proteins in response to CDK4
inhibition. In this phase of the grant, we have followed up with combination therapies targeting PDGFR and src-
kinase activation--key pathways we hypothesize play a role in the switch between cells that are sensitive and
resistant to CDK4 inhibition. A significant number of studies was required to optimize effective dosing so that
we could obtain samples for further molecular profiling. In order to relate further molecular features to the

11. phenotypic results we observe in vivo, we have also begun to perform deeper molecular profiling the xenograft
tumors.
This year, we followed-up with experiments to optimize dosing and endpoint data acquisition We
established reasonable doses by performing serial dilutions of palbociclib, saracatinib, and sunitinib. In the
DDLS8817-PDX, we found that the combination of palbociclib and sunitinib was was no more effective than
palbociclib alone. This was surprising as we had observed an increase in phospho-PDGFR-beta and may be
due to the “dirtiness” of the Sunitinib inihibitor, which is known to inhibit PDGFR and other receptor tyrosine
kinases. Based on our network analysis, the next experiment we attempted, combined palbociclib with the 2nd
generation Src inhibitor saracatinib. Unfortunately, we again had issues with dosing and the results were
inconclusive as the saracatinib alone appeared ineffective and palbociclib flatlined the tumors.
The sunitinib and palbociclib combination was tested in MPNST3-PDX. This PDX showed a strong
increase in phospho-PDGFR-alpha during treatment with palbociclib. For this study we utilized 150 mg/kg
PD991 with 40 mg/kg and 60 mg/kg sunitinib (singles, in combination, and a vehicle control). All groups had 5
animals. Sunitinib was very effective with or without palbociclib. At certain time points, it appeared that there
might be synergy between palbociclib and sunitinib. However, the addition of a slight amount of sunitinib (from
40 to 60 mg/kg) seemed to decrease tumor burden more effectively than 150 mg/kg palbociclib (see Figure 1).
As the tumor burden is very high with control and singly-treated MPNST3-PDXs, we were unable to harvest
tumors at the same time and simultaneously evaluate how the tumors respond after extended periods with the
drug. In order to gain time-matched material for further molecular analysis, we performed an additional
combination study with lower doses of sunitinib. Tumor material was harvested and analysis of this material is
ongoing.
In addition to performing several additional xenograft studies, we have also begun to profile the genomic
and transcriptomic baseline of this tumor material. We have now performed deep DNA sequencing using the
targeted sequencing IMPACT assay. We have also performed RNA sequencing on the tumor material.
Analysis is ongoing.
TRD
1.2:
Protein
network
alignment
algorithm
and
viewer;
DBP
2:
Vidal
and
Hill
TRD1, Differential networks Aim 2. We continue to develop protein-protein interaction network alignment
algorithms since publishing “GreedyPlus: An Algorithm for the Alignment of Interface Interaction Networks” in
2015 [1], the first such algorithm for protein interaction networks that includes binding site information. We
have studied protein domain and binding site evolution from a range of organisms with fully sequenced
genomes and have identified many different patterns of sequence evolution that change network architecture
at the local protein level – more so than expected. We hypothesized that we could identify a few major
sequence evolution patterns, but most examples we studied were unique. This work has led us to design a
new technology for ortholog function assessment that simultaneously considers protein and network evolution,
described in B.6. TRD1.2.
To support DBP 2 (Vidal and Hill) “Mapping the human interactome and its rewiring by disease mutations”,
we have engaged in weekly discussions with the Vidal team to consult on the analysis of their ongoing human
interactome project, in particular where their work includes differential network analysis and consideration of
binding sites.
References
1. Law B, Bader GD. GreedyPlus: An Algorithm for the Alignment of Interface Interaction Networks. Scientific
Reports. 2015;5:12074.
TRD 1.3: Facilitating the interpretation of AP-MS data as interaction networks; DBP 1: Krogan
Mass spectrometry practitioners and analysts routinely work with network models constructed from
fundamental interaction measurements. The data inform the biomedical understanding of host-pathogen
interactions, signaling networks and network rewiring in cancer, to name a few examples. This is a critical field
of research with which to provide powerful and accessible network visualization and analysis technology. This
project component is aimed at making specific improvements and implementing new features to Cytoscape to
enhance its applicability and adoption by mass spec community. The main objectives are to augment

12. Cytoscape to streamline the typical mass spec analysis pipeline and provide better access to public mass spec
data and annotation repositories relevant to researchers.
Following the guideline of our (lengthy) Nature Protocol for mass spec analysis using Cytoscape [1], we
made significant progress on streamlining and enhancing the protocol. First, in terms of identifier mapping, we
took a multistep process involving the installation and configuration of a separate app and replaced it with a
built-in context menu option added to the existing Node Table in Cytoscape. Identifier mapping, in brief,
addresses the matter of mapping between identifier systems (e.g., UniProt, Entrez Gene, Ensembl, etc) when
merging interaction data or integrating data types. This is a common problem faced by all bioinformaticians. In
the specific domain of network data in Cytoscape, we see the opportunity to provide semi-automated
assistance for users wanting to merge and integrate heterogeneous data. This is particularly relevant to mass
spec practitioners, e.g., those in the Krogan lab (DBP 1), who want to view their interaction data in the context
of other public interaction data and other annotations. The integration of identifier mapping into Cytoscape as a
built-in feature greatly enhances the user experience for mass spec practitioners as well as many other users.
See the before/after comparison of the steps required in the published mass spec Nature Protocol.
The simplification goes beyond app integration and user interface work. For example, rather than requiring
the user to explicitly connect to a database source, the new tool automatically connects to existing web service
provided by BridgeDb. And rather than requiring the user to explicitly choose a source identifier type, the new
tool guesses the identifier based on the values extracted from the column indicated by the user in the right click
action that initiated the dialog. We also included better, more common, default options for target identifier type
and the force single feature based on prior experience using and training others on the original BridgeDb app.
This is a great example of a coordinated NRNB project. Despite being spread across 4 campuses, this
project involved work by members of the Ideker lab, together with the features and resources leveraged by the
BridgeDb app, which was a Google Summer of Code project mentored by the Pico lab and implemented by a
student later hired by the Sander lab [2]. In the end, members of three of the 4 NRNB sites directly contributed
to this project, while also leveraging financial support and talent recruitment from Google.
The second major activity was the continued development of the stringApp for Cytoscape by Dr. Morris.
STRING (http://www.string-db.org/) is an important public interaction database, widely regarded by mass spec
practitioners. With input from both mass spec practitioners (DBP 1) and the developers/maintainers of the
STRING database, Dr. Morris implemented the app to take full advantage of all the unique aspects of STRING,
as described in the NAR special database issue for 2017 [3]. The stringApp has been downloaded over 7400
times since its original release in December of 2015 and is freely available at the Cytoscape App Store:
http://apps.cytoscape.org/apps/stringapp.

13. Figure 3. Screenshot of STITCH compound-protein network. This is the result of a query for Coumadin
(Warfarin®), a common blood thinner used to prevent thrombosis. Queries of proteins or compounds are
supported. The nodes in Cytoscape preserve the signature STRING style with structures and glass bobble
effects.
During this reporting period, Dr. Morris implemented critical support for STITCH as a fourth query option in
the stringApp (Figure 3). The STITCH database includes both physical interactions and functional associations
between chemical compounds and proteins (http://stitch.embl.de). Now, in addition to protein, PubMed and
disease queries, Cytoscape users can select STITCH: protein/compound query and interrogate the STITCH
database for protein-compound interactions. This new dimension of interactions allows researchers to extend
protein networks into compound space or build protein networks from a set of one or more compounds. This
feature thus nicely complements any network or protein interaction resource tools already available in
Cytoscape. It is particularly relevant to the growing demand and data deluge for drug compound screens and
metabolomics, which of course includes mass spectrometry practitioners.
Another feature added to the stringApp during this period is enrichment analysis. This was a major step in
the AP-MS protocol that once again required the installation and operation of a separate app. Now, upon
import of any network via the stringApp the user can choose to perform enrichment analysis and obtain Gene
Ontology terms and KEGG pathway results. This is a valuable addition to workflows that involve STRING or
STITCH networks.
References
1. Morris, J.H.K., G.M.; Verschueren, E.; Johnson, J.R.; Cimermancic, P.; Greninger, A.L.; Pico, A.R. Affinity
Purification-Mass Spectrometry and Network Analysis to Understand Protein- Protein Interactions. Nature
Protocol (2014) 9, 2539-54.
2. Gao J, Zhang C, van Iersel M, et al. BridgeDb app: unifying identifier mapping services for Cytoscape.
F1000Research. 2014;3:148..
3. Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein–protein
association networks, made broadly accessible. Nucleic Acids Research. 2017;45(Database issue):D362-
D368.

14.
B.6
What
do
you
plan
to
do
for
the
next
reporting
period
to
accomplish
the
goals?
TRD
1.1
In the next reporting period, we aim to modify the developed perturbation biology modeling method to
incorporate time resolved data (dynamic network analysis). The current method is developed for a single time
point and assumes the model variables to be in steady state at this time point. This is a rough assumption
since data comes from living cells that continues to grow. We will therefore use the new time resolved data to
expand upon the existing method and change the implementation of the model equations. Model variables are
in the current version of our method assured a sigmoidal temporal behavior. However, also other temporal
behaviors should be possible to capture. We will also incorporate changes to evaluate not only the steady state
solution of the equations, but the full trajectories in the calculation of the model error. These changes in the
perturbation biology method will assure that models created by the method can be used to predict temporal
drug responses and therefore make the method more generally applicable to different data sets. The network
represented by the inferred interaction parameters ideally is predictive of the effects of previously unseen
perturbations, such as design combinatorial drug interventions.
With respect to DBP 7, with the exception of possibly performing another dosing study on DDLS8817-PDX,
the mouse xenograft studies are completed. Samples were collected across many of these experiments and
have undergone molecular profiling. In the next phase of the project we will first focus on data analysis. Our
first questions center around response of the xenograft tumors to combination therapy with palbociclib. This
includes determining whether the effects of addition of sunitinib or saracatinib were synergistic or additive. We
will further integrate the molecular profiling data (phospho-tyrosine mass spectrometry, RNAseq, and DNA
copy number analysis) with the xenograft growth measurements to confirm basic expectations (e.g., sunitinib
inhibiting PDGFR activity) and also correlate molecular and phenotypic changes. We also plan to perform
network analyses to better characterize the alterations we observe with other malignant peripheral nerve
sheath and de-differentiated liposarcoma tumors profiled in the Cancer Genome Atlas (TCGA). The results of
these analyses will function as a baseline for further characterization in additional MPNST and DDLS cell lines
and possibly future xenograft studies.
TRD
1.2
Differential networks Aim 2. We continue to work on developing an evolutionary model that considers domain
and binding site changes and how these affect network alignment. Further, we are designing a new technology
for evaluating the function of proteins based on coding DNA and protein sequence evolution along with
molecular interactions involving the protein and its binding sites. We hypothesize that viewing a hierarchy of
evolutionary changes from the sequence to network levels will usefully inform protein function prediction
methods that transfer functional annotation between organisms (differential network analysis).
TRD
1.3
Our interactions with DBP 1, the Krogan lab, continue to reveal a growing list of roadblocks and challenges
with using Cytoscape with mass spec data. With our early start on this aim, as described in this and previous
reports, we are in a good position to address the bulk of this list during the overall grant period. We have
prioritized these items per their significance, the breadth of their applicability, and the feasibility of a solution.
Over the next year, we will extend the integrated identifier mapping tool described above to handle general
annotation tasks as well. These include annotating nodes with Gene Ontology terms, for example. This will be
a prerequisite for future work providing enrichment analysis as a general tool to Cytoscape.
We are also just beginning work on a gene set manager to allow Cytoscape users to import, paste or drag
gene lists into Cytoscape to use for queries (e.g., for STRING networks), for selection and for basic set

15. functions (e.g., union, intersection, difference). Many core protocols in Cytoscape, include our mass
spectrometry protocol, involve user-defined gene lists.
Finally, we are planning to tackle the logging of tasks and crash events in Cytoscape. While greatly
mitigated in Cytoscape 3, there are still scenarios that require force quit actions by the user. We plan to log
these events to help diagnose and remedy them. Similarly, a log of tasks in general would provide valuable
feedback on the major operations carried out by the bulk aggregate of users. It would also identify hard to find
or otherwise neglected features.
C.3.
Identify
technologies
or
techniques
that
have
resulted
from
the
research
activities.
Describe
the
technologies
or
techniques
and
how
they
are
being
shared.
The developed perturbation biology methodology has been publicly shared via publication. Additionally, there
is an accompanying web application (http://www.sanderlab.org/pertbio/) that is available. Users can explore
and download models produced by the analysis.
The new identifier mapping tool described in B.2. will be shared through the free, open source distribution
of Cytoscape 3.5.0+ as of March 2017. The stringApp is freely available as an open source app for Cytoscape
at http://apps.cytoscape.org/apps/stringapp. The app now includes STITCH support as described in B.2. The
stringApp has been downloaded over 7400 times since it’s release 13 months ago.

16. TRD
2:
Descriptive
to
Predictive
Networks
B.2.
What
was
accomplished
under
these
goals?
TRD2.1:
Predicting
clinical
outcome
using
patient
similarity
networks;
DBP
5:
Friend
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis,
disease subtyping and treatment response prediction. A general purpose and clinically relevant prediction
algorithm should be accurate, generalizable, be able to integrate diverse data types (e.g. clinical, genomic,
metabolomic, imaging), handle sparse data, be compatible with patient privacy protection systems and be
intuitive to interpret. We have recently developed netDx (http://netdx.org/), a supervised patient classification
framework based on patient similarity networks that meets the above criteria. netDx models input data as
patient networks and uses the GeneMANIA machine learning algorithm that we previously developed for
network integration and feature selection. We demonstrated the utility of netDx by integrating gene expression
and copy number variants to classify breast cancer tumour class, achieving an accuracy (~85%) similar or
better (depending on the class) than previously published methods. Further, we have been able to successfully
predict Autism Spectrum Disorders (ASD) phenotype from germ line DNA for a subset of ASD patients(Figure
1). netDx uses pathway features to aid biological interpretability and results can be visualized in Cytoscape as
an integrated patient similarity network to aid clinical interpretation.
Figure 1. Predictive power of netDX is better than contemporary methods: ASD case. Mean test performance
over three resamplings. Predicted status is informative beyond genetic ancestry (ANOVA chisq-test,
p=2.76x10-10
). GBT = pathway-level FET (cnvGSA). RFCF = Random forests (Engchuan, et al. 2015 BMC
Genomics). Reproduced from NetBio SIG presentation.
To support DBP 5: Sage Bionetworks: Molecular stratification of colorectal cancer and DREAM challenges,
we have revisited all major DREAM challenges where data are available and where the challenge experimental
design is compatible with netDX’s classification engine (two class classification). We have reported on netDx
[1] and presented on netDx at the NetBio SIG Meeting in 2016 [2].
References:
1. Shraddha Pai, Shirley Hui, Ruth Isserlin, Hussam Kaka, Gary Bader. netDx: Patient classification using
integrated patient similarity networks. bioRxiv 084418; https://doi.org/10.1101/084418
2. https://f1000research.com/slides/5-1710

17. TRD2.2:
Predicting
cellular
response
to
perturbation
with
network-­‐guided
regression;
DBP
8:
Pommier
Part
I
Background: The overall goal of this project is the identification of biomarkers involved in the progression of
cancer and the response to pharmaceutical treatment. This goal is accomplished through the used for
regression-based methods that subject to biological network constraints so that biomarkers can be understood
in the context of regulatory processes.
Current progress: Applied to our DBP 8 use case, “Drug response prediction for the NCI compound library,” a
major first step in this project has been data preparation of the NCI-60 and Pathway Commons data. This was
done through the development of the rcellminer/paxtoolsr R packages, respectively, to simplify the usage of
this data from an R programmatic environment. Previous to this development the NCI-60 data was provided in
spreadsheets laid out in varying formats. In addition to the direct conversion of the data from the NCI-60
CellMiner website (http://discover.nci.nih.gov/cellminer) additional elements of metadata were included in the R
package, including: structures, repeat drug screen data, mechanisms of action for compounds, and drug
approval information not found on the website.
A second ongoing focus during this reporting period has been the continued expansion of data relating to
the NCI-60 to all as wide as possible exploration of biological processes as possible. During the reporting
period, analysis of the methylation data for the NCI-60 was performed. The analysis of RNA-Seq dataset for
the NCI-60 is ongoing and should be completed within the next reporting period and made publicly available for
further usage within this project.
During the last reporting period, we have now collected the experimental data for about 39 compounds
first screened on the NCI-60 then re-screened on the Sanger Genomics of Drug Sensitivity in Cancer (GDSC)
cell lines. This provides us data for ~750 cell lines were screened, a low number (7 compounds) of the
compounds sent precipitated during screening preventing their analysis.
Additionally, we have also submitted a manuscript on the analysis of the NCI-60 SWATH mass
spectrometry (MS) data produced by the Aebersold group at ETH Zurich. Our analytic contribution was the use
Elastic Net regression analysis methodology using subsets of available feature sets (e.g. gene expression,
mutations, and protein abundances). With respect to this project, while these are not network-constrained
regression analyses there are allowing us to develop an important baseline by which we will compare future
results involving network constraints.
We have made some preliminary progress on the network-constrained regression methodology during this
reporting period. Our starting point is the recently published GELnet method, and we have a working
demonstration of the method for one drug found in the NCI-60 using expression data. The GELnet method
should highlight novel predictors of drug response.
Part
II
Overview: Deep learning has achieved tremendous success in various biology applications such as drug
discovery, DNA/RNA protein binding and noncoding variants effects prediction. In biology, accurate prediction
is not enough, the cell is never “conquered” until human being understand why it behaves in that way. A major
challenge to tackle this problem is to develop an ‘in silico’ model which is able to simulate the biological
process of cell happened ‘in vivo’ with respect to the actual cellular structure. A number of successful
approaches have modeled the cell’s transition from genotype to phenotype by using prior knowledge in the
form of molecular networks. In these approaches, genetic variation is first mapped onto molecular networks;
affected subnetworks are then associated with phenotype. Such important information is then learned by a
supervised machine learning model using the diffused signal as features. Here, we have constructed a “white
box” model, called DeepCell, which uses the hierarchical structure of the cell to simulate cell behavior with both
high accuracy and interpretability. Prior biological knowledge is organized into a hierarchical form and the
structure of the predicting model is also constructed based on that hierarchy. DeepCell can learn complex
patterns from large datasets while still keep low computational complexity, thus prohibit overfitting. To interpret
the model, one can observe how the input signal propagates bottom-up through the hierarchical structure and
activate different subsystems at multiple scales to make final predictions.

18. Results: In this work, we focus on the task of simulating pairwise genetic interactions among ~3000 non-
essential genes in the budding yeast, Saccharomyces cerevisiae, in which the combined loss of both genes
might lead to unexpectedly slow or fast relative growth rate comparing with the loss of either gene alone. We
used two hierarchical structures to guide the deep neural network model including Gene Ontology (GO)
curated from Saccharomyces literature and data-driven ontology assembled from Saccharomyces datasets
using network-extracted methods. DeepCell can accurately predict phenotypes including both growth rate and
genetic interaction score across a range of genetic interaction scores (Figure 2). In comparison with all of these
approaches, DeepCell using GO and data-driven ontology both achieved substantially greater correlation
between predicted and measured genetic interaction scores.
Figure 2. a, Measured versus predicted cell viability relative to wild type (WT = 1). b, Measured versus
predicted genetic interaction scores for each double gene disruption genotype; genetic interactions between
the disrupted genes can be positive (epistasis), zero (non-interaction), or negative (synthetic sickness or
lethality). c, Predictive performance of neural networks, measured as the correlation between measured and
predicted genetic interaction scores on the Costanzo dataset (first four bars). Network structures are based on
prior knowledge of the hierarchy of cellular subsystems, as inferred from ‘omics datasets (CliXO) or from
literature curation (GO). Also shown is the average performance of neural network structures for which gene-
to-subsystem mappings have been randomly permuted. Performance is also compared to previous methods
for predicting genetic interactions (second four bars). d, Predictive performance as a function of the number of
neurons per subsystem (CliXO or GO term). The performance measure and four neural networks are identical
to (c).

19. B.6.
What
do
you
plan
to
do
for
the
next
reporting
period
to
accomplish
the
goals?
TRD2.1
Predictive networks Aim 1. We submit a publication about this in 2016 and are now working on revisions to be
submitted in 2017. We are now focused on the following extensions: 1) extend our results to other disease
areas; 2) extend feature engineering work to consider epigenomics data and non-coding genome regions.
TRD2.2
Part
I: We will continue apply the developed dataset components to the development of the network-
constrained regression models in the upcoming reporting period, and make datasets more widely available as
they become published. In coordination with DBP 8, we will also look at additional upcoming NCI-60 datasets
(e.g. RNA-seq) to see their utility for this regression methodology. We will continue to look at pre-existing
methodologies to examine their properties and what improvements may be warranted.
Part
II: Unlike standard ANNs, DeepCell was tied directly to cell structure, raising the possibility that its
predictions could be interpreted biologically. In the next step, we are going to study whether the model can be
opened to dissect the internal cellular subsystems and functional logic responsible for governing a particular
genotype-to-phenotype response? To address this question, we will study how to interpret the predicting
process by using DeepCell. We will begin by scoring the importance of each subsystem to DeepTranslate’s
overall genotype-phenotype function. Besides global ranking, we will also explore the most important
subsystems from the GO hierarchy, examining their internal states and their functional logic.
C.3.
Identify
technologies
or
techniques
that
have
resulted
from
the
research
activities.
Describe
the
technologies
or
techniques
and
how
they
are
being
shared.
TRD2.1
We have developed the netDX technology and will disseminate it at the netdx.org website and GitHub, under
an open access software license. The technology is implemented in R and Java, as an easy to use and well-
documented R package.
TRD2.2
We anticipate that several useful resources will be generated by the proposed research. We will provide a new
deep learning training algorithm to train the hierarchy-guided deep neural network. We will provide a new
analysis pipeline to help people the behavior of their supervised machine learning model. We will also provide
a web server where users can not only predict growth related phenotypes using our trained deep learning
model but also interpret the logic of prediction.

20. TRD
3:
Multi-­‐scale
Networks
B.2.
What
was
accomplished
under
these
goals?
TRD
3.2:
Functionalized
gene
ontologies
as
a
hierarchy
of
functional
prediction;
DBP
3:
Cherry
Development and validation of an iterative procedure for incorporating new data into a data-driven ontology. In
the last reporting period, we began developing a general progressive procedure, Active Interaction Mapping, to
guide assembly of the hierarchy of functions (ontology) encoding any biological system. Since then, we have
published this work [1] and have made the procedure available at http://atgo.ucsd.edu. In this work, we
assembled an ontology of functions comprising autophagy, a central recycling process implicated in numerous
diseases. We performed subsequent experimental validation of the ontology, including newly identified roles
for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole
targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. This work was co-authored by
our DBP 3 with Michael Cherry [1].
Construction of a data-driven gene ontology in human.
Whereas our previous work was focused in yeast, we have
recently constructed the first data-driven gene ontology in
human. As input to building this ontology, we took 908
experimental studies covering 98% of human coding genes.
These data were drawn from several databases, including
Gene Expression Omnibus (668 microarrays), GeneMANIA
(201 genetic/protein interaction networks), GTEx (35 co-
expression networks). Using our Active Interaction Mapping
pipeline, we integrated these datasets into a unified gene-
gene similarity network and then hierarchically clustered this
network to assemble a human gene ontology (called
HNeXO).
Parallelized, GPU-based algorithm for ontology
construction. We have been optimizing our algorithm for
constructing an ontology. Currently, it takes about a day to
assemble a data-driven gene ontology in yeast (~6000
genes) and several days for one in human (~20,000 genes).
We aim to reduce this runtime down to the span of hours by
parallelizing the computation. To do this, we have
reformulated the construction of an ontology as a series of
matrix computations, for which there are known algorithms
for massive parallelization and efficient memory caching.
Our approach fully exploits the capacity of parallelism on a
multi-CPU platform and is easily generalized to Graphics
Processing Units (GPU).
References
1. Kramer MH, Farré JC, Mitra K, et al. Active Interaction Mapping Reveals the Hierarchical Organization of
Autophagy. Mol Cell. 2017.
TRD3.3:
Bridging
ligand-­‐receptor
networks
to
cell-­‐cell
communication
networks;
DBP
9:
Zandstra
We have undertaken new research and development work to infer cell-cell interaction networks. In particular,
we have extensively used single cell RNA-seq data to infer higher resolution cell-cell networks and have
developed applications to cancer stem cell biology and regenerative medicine (e.g. DBP 9), both areas where
cell communication is important for tumour or normal tissue development. For single cell RNA-seq, we start by
clustering the single cells to define cell types. Clusters representing cell types are identified by the expression
of known cell type markers and previously unrecognized clusters are, by default, linked to the nearest known
cell type (e.g. neuron subtype A, B, C…) Each cell type is analyzed for the expression of surface receptors and
Data-driven gene ontology in human
(HNeXO). To understand how HNeXO
compares with curated knowledge in the
Gene Ontology (GO), we searched for
matching HNeXO and GO terms. HNeXO
recapitulates thousands of GO terms and
also discovers new terms that are
supported by data but have no

21. ligands to infer connections between them. These represent hypotheses about cellular communication for
experimental follow up.
To support DBP 9: Engineering blood for regenerative medicine, we are automating our cell-cell interaction
network inference pipeline. This is important as the regenerative medicine community in Toronto received a
transformation grant ($114M CAD) to expand this scientific research area. As a result, many more
developmental biology research groups are requesting cell-cell interaction network analysis. As a major
milestone, a neural developmental biology group used this method to identify three new neural development
factors (Figure 1) [1].
Figure 1. Integration of the E13/14 cortex ligand data with the transcriptome-based cortical communication
model and the combined transcriptome-cell-surface proteome communication model. Red nodes denote
ligands predicted in the transcriptome-based model that were expressed in the E13/14 cortex and also have
receptors identified by cell-surface proteomics. Nodes surrounding the yellow CP (Cortical Precursor) and CN
(Cortical Neuron) nodes represent predicted autocrine ligands for CPs and CNs, respectively. Nodes located
between the yellow CP and CN nodes are predicted paracrine ligands. Edges indicate direction of
communication. Reproduced from ref 1.
References
1. Yuzwa SA, Yang G, Borrett MJ, et al. Proneurogenic Ligands Defined by Modeling Developing Cortex
Growth Factor Communication Networks. Neuron. 2016;91(5):988-1004.
B.6.
What
do
you
plan
to
do
for
the
next
reporting
period
to
accomplish
the
goals?
TRD3.2
Data-driven ontologies of other biomedical data types, including drugs and phenotypes. Our current work
has focused on assembling and applying gene ontologies, in which we group genes based on similar functions
within the cell. Likewise, drugs can be organized into drug classes based on similar chemical structure, gene
targets, or functional effect. Moreover, clinical signs and symptoms can be organized into diseases and
disease classes. In the next reporting period, we will use available pharmacological and clinical datasets to
assemble data-driven ontologies of drugs and phenotypes.
Construction of a cancer-specific human gene ontology. One of the limitations of the Gene Ontology is that
it encompasses all species, tissues, and cell types. Context-specific knowledge is difficult to extract from GO.
In our Active Interaction Mapping work, we showed that it is possible to assemble a context-specific ontology
(yeast autophagy). In the next reporting period, we will integrate cancer ‘omics data into HNeXO to make a
cancer-specific gene ontology.
TRD3.3
We are currently working to improve our network inference using additional information on cell-cell
receptor-ligand pathways. For instance, if a pathway downstream of a receptor is active, it strengthens the

22. inference that the receptor is active in the network. We also continue to work to adapt this method to the
analysis of single-cell RNA-Seq data. This mainly involves refinement of cell type inference from single cell
RNA-seq data. As the technology used to generate this exciting new data type is rapidly evolving, we are
spending a large amount of time keeping up with new data sets and computational methods.
C.3.
Identify
technologies
or
techniques
that
have
resulted
from
the
research
activities.
Describe
the
technologies
or
techniques
and
how
they
are
being
shared.
An online, interactive viewer of the Active Interaction Mapping procedure and its application to yeast
autophagy at http://atgo.ucsd.edu.
A data-driven gene ontology in human. We will also make this ontology available through an online,
interactive viewer.
Parallelized ontology construction. We will make code available on GitHub once completed.

23. CSP-­‐Compilation
B.2
What
was
accomplished
under
these
goals?
Sander
Group
In this last period the Sander lab has focused on a number of collaborations as part of NRNB (see CSP table
below). The collaborations fall several categories: the expansion of existing pathway database resources, work
on cytoscape.js to improve SBGN support, and pathway and network analysis of breast, prostate and pan
cancer datasets.
In collaboration with Dr. Joan Brugge at HMS, we investigating targeted drug combination therapies for
triple negative breast cancer. Triple negative breast cancer continues to see over 150,000 new diagnoses
annually, and despite initial responses to the current established chemotherapy protocols, treatment resistance
ultimately emerges in the vast majority of cases. The significant patient-to-patient heterogeneity in the genetic
alterations of triple negative cancers (TNBC) presents a major challenge for the development of effective
targeted therapies. The goal of this proposal is to identify novel drug combinations that will improve outcomes
in TNBC patients.
We are accumulating and organizing TNBC cell line and patient tumor data from online databases
(cBioportal: TCGA and CCLE datasets), including mutation profiles and gene and protein expression levels. In
concert, we are collecting and organizing information from TNBC clinical trials, including treatment regiments,
objective response rates, and response biomarkers. These clinical data will be integrated with drug response
data on TNBC cell lines, including drug sensitivity (IC50) and target pathways (pathway data from Pathway
Commons). Using pathway and network analysis on the background of the clinical information, we will identify
rational combinations of drugs that will either target multiple proteins in a pathway, multiple pathways, or both
of these methods. These combinations will then be tested first in TNBC cell lines and later in patient-derived
xenograft models that better represent the heterogeneity of TNBC.
In collaboration with Dr. Rileen Sinha at Mount Sinai, we are also developing methods for calculating
similarity between cancer samples. Cell lines derived from human tumors are often used in pre-clinical cancer
research, but some cell lines may be too different from tumors to be good models. Genomic and molecular
profiles can be used to guide the choice of cell line suitable for particular investigations, but not all features
may be equally relevant, so any resulting methodology should take this concern into account. Understanding
the similarity between cancer samples is not limited to the comparison between cell lines and patient samples.
Particular projects may require comparison within different sets of patient or cell line samples. For these
projects, a generic approach using genomic and molecular profiles would be useful.
TumorComparer is a computational method and web service developed for comparing cell lines and
tumors with the flexibility to place a higher weight on functional alterations of interest. The first application of
TumorComparer was used to compare 260 cell lines and 1914 tumors of six cancer types from TCGA, using
weights emphasizing recurrent genomic alterations. These cell lines were ranked by their similarity to tumors
and identify apparently unsuitable outlier cell lines, including some that are widely used.
Method for developing patient similarity network: Given discretized data representing genomic alterations
(i.e. mutations and copy number alterations). Samples are represented by feature vectors and and a weight
vector for feature weights. Their weighted similarity is calculated using weighted asymmetric matching, which
measures the similarity between two samples after discarding the 0-0 matches (hence “asymmetric”). The
similarity is calculated as the ratio of the intersection to the union of the subsets of features for which the two
samples have non-zero values.
The weighted similarity method in the future may be useful to assess genomic-molecular patient profiles for
personalized choice of clinical trials or therapy in the construction of a patient similarity network. For the
construction of a patient similarity network, we plan to use pairwise weighted similarities that instantiate a
particular focus of interest, e.g., therapy relevant genetic alterations.
Bader
Group
The Bader group engages in an impressive number of NRNB related collaborations each year (see CSP table
below). The CSP work over the past year includes a cell-cell communication project and pathway analysis of
single cell data. NRNB tools are underlined in the highlights below:

24. Crosstalk between cancer associated fibroblasts (CAFs) and epithelial cancer cells in Head and Neck
cancer.
Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cause of cancer-related death
worldwide. Recently, the tumor microenvironment has been shown to have a significant impact on disease
progression in several tumor types, however, little is known about it in the context of HNSCC. Carcinoma-
associated fibroblasts (CAFs) make up a significant proportion of the tumor stroma, where they facilitate tumor
cell proliferation and invasion. Elucidating the molecular programs of interaction between these two cell
populations will help us to understand how they communicate with each other. Using laser-capture
microdissection (LCM) pure populations of tumor cells and CAFs were obtained from patient tissue sections,
along with their normal counterparts from matched tumor-free tissue. Laurie Ailles et al. at the Ontario Cancer
Institute, used gene expression microarrays to generate transcriptomic profiles of these cell populations. In our
collaboration together, we used these data to identify potential molecular interacting partners utilized by tumor
and stromal cells to communicate with each other.
The cell-cell interaction networks were constructed using the gene lists generated in the transcriptomic
analysis. The maps were made in Cytoscape using the tumor specific and the CAF specific genes (FDR <
0.05). The genes in both lists were classified as “ligand” or “receptor” according to Gene Ontology terms –
“cytokine activity”, “hormone activity” and “growth factor activity” for ligand and “receptor activity” for receptor
(Qiao et al.) iRefIndex (Razick et al.), a database of receptor-ligand interactions annotated based on published
studies, was used as a reference to build the interaction maps. Gene-Set Enrichment Analysis (GSEA) was run
against the ranked list of genes from top up-regulated in CAF versus tumor to down-regulated using the t-test t
values. The NRNB Cytoscape app EnrichmentMap was used to visualize the results of the enrichment analysis
(Figure 1).
Figure 1. a) Ligand-Receptor network created using Cytoscape. The outer circle represents CAF genes
and the inner circle represent tumor genes. b) Cytoscape EnrichmentMap representing enriched gene-sets
with FDR <= 0.05 in genes up-regulated in CAFs (red) and genes up-regulated in the tumor samples (blue).
Colon
cancer
stem
cell
characterization
and
analysis
Colorectal cancer is the second leading cause of cancer death in the United States. Cancer stem cells are
suspected to play a major role in initiation and recurrence in this disease. Dr. Catherine O’Brien’s lab is
characterizing colon cancer stem cells to identify sensitive points that can be targeted to kill the cells. This

25. project analyzes POP92 cells, a patient-derived colon cancer stem cell line. By combining and collapsing the
transcriptome data of single cell RNAseq data from a colorectal cancer line into pathway activities, the Bader
lab has successfully identified 4 - 6 distinctive populations within the POP92 cells. Through correlation analysis
comparing each population with published colorectal CSC datasets, one of the populations has consistently the
highest correlation with known CSCs. Further characterization of this potential CSC population has identified
the activation of pathways in telomere and mitochondrial function while pathways involving apoptosis,
autophagy, differentiation, and development are repressed.
Single cell RNA-Seq was performed for 96 Wnt-low and 96 Wnt-high single cells from a colorectal cell line
cells (Fluidigm). Alignment of the fastq files was performed using STAR with human genome reference
GRCh37 to generate raw counts for gene expression values. Multiple clustering algorithms (W: Ward, K:K-
means, N:NMF) were employed to identify distinct populations of single cells. Pathway analysis of gene
expression data using the NRNB tool EnrichmentMap was used compare different clusters of cells and to
reveal differences in cell cycle and differentiation pathways (Figure 2).
Figure 2. a) FACS sorting of single cell populations; b) Clustering results from 3 different methods; c)
EnrichmentMap comparing 2 clusters of cells.
In this figure, we can see the difference between two groups of potential CSCs. CSC K4 (red nodes) has
elevated cell cycle, mitosis, recombination repair, non-recombinational repair, base repair, DNA replication,
DNA integrity, and TCF transactivating pathways. CSC K3 (blue nodes) has elevated embryo, endoderm, eye,

26. secretion, wnt, inhibition of apoptosis, extrinsic apoptosis, mesenchymal stem, and lymphocyte differentiation
pathways. From these pathways, it appears that CSC K4 is actively dividing with high level of precise DNA
replication. CSC K3 appears to be relatively more differentiated but with embryonic and mesenchymal stem
pathways activated.
Ideker
Group
The table presents projects that are completed as collaborations with various faculty at UCSD and local
research institutions. All analyses were completed or are on-going as a part of a recharge fee-for-service.
Here is a brief description of three completed projects from the reporting period:
1) Pathway analysis of RNA Sequencing (Elisabeth Mertsching, Atyr Pharma) Results: Human T cells
were stimulated in presence of our test article (TA) or vehicle. After 24 hours, cells were collected, RNA
isolated and sent to GeneWiz for RNA sequencing. Results were analyzed with Limma and a list of genes
differentially expressed was established. Goals: Using this list, identify upstream targets of the TA. Determine
which pathway or pathways are modulated by our TA and identify proteins modulating expression of genes
affected by the TA. Status reports: 04/01/2016: Pathway analysis delivered via Google Drive. 04/09/2016:
Responded to two emailed questions from customer re Cytoscape usage; delivered pdf of additional
Cytoscape guidance. 04/11/2016: Responded to two emailed questions from customer re Cytoscape usage,
provided guidance on GeneMANIA. 04/19/2016: Delivered signed report pdf and additional guidance on
GeneMANIA. Deliverables: Detailed report pdf, colocalization methods pdf, cleaned differential expression csv
with ensembl and entrez gene ids, differential expression csv for all unique entrez gene ids, toppgene
enrichment report pdf, cytoscape network session, csv of heat propagation results. Guidance on how to use
Cytoscape and GeneMANIA.
2) NGS data integration and network analysis (Sanjay Nigam). Objective: Drug transporter networks and
data integration Updates: 11/21: Sent final deliverables: 1. Updated report of analysis, including addition of 4
control TF's/genes (Hoxb7, Sall1, Pax6, Ret) to the network proximity analysis. 2. Excel file containing
information about the Hnf4a subnetwork at each stage of development, including log2 fold change from E20,
community membership and associated GO term. 3. Integrated systems bio figure, with real miRNA/mRNA
expression data replacing simulated data from last week.
3) HD Network analysis (Vivian Hook). Objective: Mutant Huntington Protein Interactions Networks in
Animal Models of Huntington’s Disease. Integrate Htt interactor proteins in non-human animals with existing
knowledge of protein-protein interactions in the literature. Perform network analysis of these Htt-interactor
proteins overlaid on literature PPI networks. Identify groups of highly connected genes related to Htt, using
network propagation techniques and clustering methods. Pathway analysis of these highly connected genes,
particularly in pathways of interest, including mechanisms of cell death, intracellular trafficking and transport,
synaptic cell-cell communication and interactions, energy metabolism, cell viability. Build wild-type and mutant
Htt protein interaction networks, compiled from literature, and evaluate differences in connectivity and network
structure, using clustering and network propagation methods.
Pico
Group
Since the renewal, the Pico group has initiated many new NRNB collaborations as a service model through
Gladstone’s Bioinformatics core (see CSP table below). Many of these collaborations fall into the standard
category of assisting with network and pathway analysis, but over a wide range of compelling topics. For
example, in collaboration with Drs. Gan and Akassoglou at Gladstone and UCSF, we identified interactions
networks involving drugs and proteins relating to the role of innate immune response and oxidative stress in
models of Alzheimer’s disease. Also with Dr. Akassoglou, we modeled novel pathways for neuroinflammation
implicated in Multiple Sclerosis. With members of Dr. Deepak Srivastava’s group, we applied NRNB tools to
help characterize alternative protocols for cardiac reprogramming, an essential technology for stem cell
therapies in cardiac related diseases and conditions. And in a collaboration with Dr. Sonja Schrepfer of UCSF,
we provided functional enrichment analysis and pathway visualization toward the study of the effects

27. microgravity on cardiac tissue expression. The microgravity environment, currently relevant to astronaut health,
was provided by the International Space Station for multiple rounds of transported mice.
In addition to service-based collaborations, we managed the code development collaborations between
~45 mentors and ~18 students during this reporting period through the Google Summer of Code and NRNB
Academy programs, combined. This work entails recruiting mentors, marshaling project ideas into the NRNB
GitHub tracker, preparing the mentoring organization application to Google, selecting student-mentor pairs,
and guiding mentors and students through a successful project, including formal evaluations and incorporating
new open source code repositories into https://github.com/nrnb/.
CSP
Table
for
Reporting
Period
Total of 93 new and ongoing project during this reporting period. In yellow are the 41 projects that concluded
during this period.
Collaborating
Investigator
Investigator
Institution
Project Title Resource
Personnel
Start /
Finish
date
External
funding
status
Publications
John Kelsoe University of
California, San
Diego
RNAseq and network
analysis
Aaron Chang 2017- NIH
UL1TR001442
of CTSA
Vivian Hook University of
California, San
Diego
Mutant Huntingtin Protein
Interactions Networks in
Animal Models of
Huntington’s Disease
Aaron Chang 2017- NIH
UL1TR001442
of CTSA
Catherine
O’Brien
University
Health
Network,
Toronto,
Canada
Pathway and Network
analysis of single cell
RNAseq data from Colorectal
Cancer line using Cytoscape/
EnrichmentMap
Gary Bader/
Veronique
Voisin
2016-
Cindy Guidos Hospital for
Sick Children,
Toronto,
Canada
Pathway and Network
analysis of RNAseq data
using Cytoscape/
EnrichmentMap: comparing
different clinical subgroups of
B-ALL
Gary Bader/
Veronique
Voisin
2016-
David
Jimenez-
Morales
(Nevan
Krogan)
UCSF Network visualization support
and technology development
Alex Pico,
Adam Treister
2016-
Derek van der
Kooy
University of
Toronto,
Toronto,
Canada
Pathway and Network
analysis of single cell
RNAseq data from Retina
Photoreceptors from Stem
Cells
using Cytoscape/
EnrichmentMap
Gary Bader/
Veronique
Voisin
2016-
Faten Sayed
(Li Gan)
Gladstone
Institutes
Activity of Trem2 knockouts Alex Pico,
Kristina
Hanspers
2016-

28. Gelareh
Zadeh; Ken
Aldape
University
Health
Network,
Toronto,
Canada
Pathway and Network
analysis of meningioma
RNAseq data using
Cytoscape/ EnrichmentMap.
Gary Bader/
Veronique
Voisin
2016-
Gordon Keller Princess
Margaret
Cancer
Centre,
Toronto,
Canada
Pathway and Network
analysis of RNAseq data
using Cytoscape/
EnrichmentMap: comparison
endoderm differentiation from
embryonic stem cells.
Gary Bader/
Veronique
Voisin
2016-
Joan Brugge HMS Ludwig
Center
Pathway Analysis in Triple
Negative Breast Cancer
Chris Sander 2016- NCI
John Dick Ontario
Cancer
Institute,
Toronto,
Canada
Pathway and Network
analysis of proteomics data
using Cytoscape/
EnrichmentMap: comparison
between mirs co-
overexpression (VOD) and
normal cord blood samples.
Gary Bader/
Veronique
Voisin
2016-
Kristin Hope McMaster
University,
Hamilton,
Canada
Pathway and Network
analysis of RNAseq data
using Cytoscape/
EnrichmentMap: comparison
between PLAG1
overepxression and normal
cord blood samples.
Gary Bader/
Veronique
Voisin
2016-
Lihong Zhan
(Li Gan)
Gladstone
Institutes
Astrocyte response to
microglia depletion by drug
treatment
Alex Pico,
Kristina
Hanspers
2016-
Meghana
Gadgil
UCSF Metabolic pathways in
models of type 2 diabetes
Alex Pico 2016-
Nikolaus
Schultz
Memorial
Sloan-
Kettering
Cancer Center
TCGA PanCanAtlas:
Pathways Group
Augustin Luna 2016- TCGA, NCI
Sanjay Nigam University of
California, San
Diego
Drug transporter networks
and data integration
Aaron Chang 2016- NIH
1U54HD09025
9
Sonja
Schrepfer
UCSF Effects of microgravity on
cardiac tissue expression
Alex Pico 2016-
Arman Aksoy Memorial
Sloan-
Kettering
Cancer Center
Develop Pathway Database
Converters for the Expansion
of the Pathway Commons
Database
Augustin Luna 2015-

29. Ben Good,
PhD
Scripps
Research
Instittute
Playmatics portal for science
games
Alex Pico 2015-
Charles Perou University of
North Carolina
Pathway and Network
Analysis of Breast Cancer
Giovanni Ciriello 2015-
George
Chacko, Jim
Onken
NIH Office of
Data Analysis
Tools and
Systems
Network based grantee
portfolio analysis
Alex Pico 2015-
John Dick Ontario
Cancer
Institute,
Toronto ,
Canada
Protein expression in CD34+
cells overexpressing mir125a
Gary Bader/
Veronique
Voisin
2015-
John DIck Ontario
Cancer
Institute,
Toronto ,
Canada
Processing RNAseq data
from cord blood samples
corresponding to the whole
hematopoietic hierarchy
Gary Bader/
Veronique
Voisin
2015-
John DIck Ontario
Cancer
Institute,
Toronto ,
Canada
Energy metabolism in normal
and malignant hematopoietic
stem cells
Gary Bader/
Veronique
Voisin
2015-
John DIck Ontario
Cancer
Institute,
Toronto,
Canada
Clustering and pathway
analysis of patients with
relapsed AML
Gary Bader/
Veronique
Voisin
2015-
John DIck/
Jean Wang
Ontario
Cancer
Institute,
Toronto ,
Canada
analysis of the AML
subpopulation using CD200
Gary Bader/
Veronique
Voisin
2015-
John DIck/
Jean Wang
Ontario
Cancer
Institute,
Toronto ,
Canada
analysis of the AML
subpopulation using CD200
Gary Bader/
Veronique
Voisin
2015-
Laurie Ailles Ontario
Cancer
Institute (OCI),
Toronto,
Canada
Standard training in one-on-
one session for gene-set
enrichment using
Cytoscape/EnrichmentMap ,
ovarian cancer CAF vs NAF
Gary Bader/
Veronique
Voisin
2015-
Massimo Loda Dana-Farber
Cancer
Institute
Comprehensive Genomic
Analysis/Metabolic of
Prostate Adenocarcinoma
Ed Reznik 2015-

30. Metin Can
Siper (Uğur
Doğrusöz,
Onur Sümer)
Bilkent
University
Computer
Science
Department,
Ankara,
Turkey
Improving Cytoscape.js
Based Viewer for SBGN
Process Description
Diagrams with Better Layout
and Advanced Complexity
Management Operations
Augustin Luna 2015-
Patricia
Defechereux
Gladstone
Institutes
Pathway and Network
Analysis of HIV Groups
Alex Pico 2015-
Robert
Rottapel
University of
Toronto,
Canada
Pathways and processes
active in Ovarian Serous
Cancer
Gary Bader/
Veronique
Voisin
2015-
Ruedi
Aebersold
ETH Zurich Pathway and Network
Analysis of Prostate Cancer
Alex Root 2015- MSKCC
Sheila Singh McMaster
Stem Cell and
Cancer
Research
Institute,
Canada
GBM CD133+/- vs NSC
CD133 +/-
Gary Bader/
Veronique
Voisin
2015-
Sheila Singh McMaster
Stem Cell and
Cancer
Research
Institute,
Canada
Bmi1 knockdown effects Gary Bader/
Veronique
Voisin
2015-
Jean Wang,
John E. Dick
Ontario
Cancer
Institute
Large scale analysis of stem
cell enriched fraction of adult
acute myeloid leukemias
Gary Bader,
Veronique
Voisin
2013- Ontario
Institute for
Cancer
Research
Benjamin A.
Alman
SickKids Network Analysis on Stem
cells from musculoskeletal
tumors
Veronique
Voisin, Gary
Bader
2012- Ontario
Institute for
Cancer
Research
Charles
Sawyers
MSKCC Pathway and Network
Analysis of Prostate Cancer
Chris Sander,
Debbie Bemis,
Alex Root
2012- NCI, NIH
Mark Ginsberg UCSD Composition of the Integrin
Activation Complex
Trey Ideker 2012- NIH
Mathew
Meyerson
Harvard
University
Pathway and Network
Analysis of Lung Cancer
Chris Sander,
Debbie Bemis
2012- TCGA
Genome Data
Analysis
Center, NCI
22960745
Stephen
Friend
Sage
Bionetworks
Integrating Cancer Datasets
for Predictive Model
Development and Training,
Rheumatoid arthritis
treatment prediction
Trey Ideker
Gary Bader
Chris Sander
Alex Pico
2012- NCI U54
CA149237
23671412,
23177740,
22836096,
21390021

31. Steven Kay UCSD Cell-autonomous circadian
clock of hepatocytes drives
rhythms in transcription and
polyamine synthesis
Trey Ideker 2012- NIH
William
Stephen
Hancock
Northeastern
University
ERBB2 driven cancer Trey Ideker 2012- Multiple 23647160
Andrew Emili University of
Toronto,
Canada
Mechanistic investigation of
microRNA-mediated
regulation of dilated
cardiomyopathy
Gary Bader 2011-
Jianfeng Li
(Robert W.
Sobol)
University of
Pittsburgh
Cancer
Institute,
Hillman
Cancer
Center,
University of
Pittsburgh
DNA Repair dependent
transcriptome reprogramming
& investing synthetic lethal
interactions with DNA repair
genes
Trey Ideker 2011- NIH
Marc Vidal Dana Farber Mapping the human
interactome and its rewiring
by disease mutations
Gary Bader 2011- NHGRI P50
HG004233,
NHGRI U01
HG001715,
NIGMS R01
GM109199
23549480,
19841731
Mike Cherry,
Judith Blake
Stanford,
Jackson Labs
Gene Ontology Consortium,
Saccharomyces Genome
Database
Trey Ideker,
Gary Bader
2011- NHGRI P50
HG004233,
NHGRI U01
HG001715,
NIGMS R01
GM109199
23242164
Peter
Zandstra
University of
Toronto,
Canada
Mapping and analyzing cell-
cell interactions in the
hematopoietic system
Gary Bader 2011-
Quaid Morris University of
Toronto,
Canada
Development of GeneMANIA
gene function prediction
software
Gary Bader 2011- Genome
Canada
23794635
Sheila Singh Stem Cell and
Cancer
Research
Institute (SCC-
RI) at
McMaster
University
Characterization of the
Heterogeneity of Human
BTICs
Gary Bader 2011- Ontario
Institute for
Cancer Stem
Cell Research
Ruedi
Aebersold
ETH Analysis of differential genetic
networks
Trey Ideker 2010- Swiss Federal 21127252
(Science, 176)
Katerina
Akassoglou
Gladstone
Institutes
Pathway modeling for
neuroinflammation model of
Multiple Sclerosis
Alex Pico,
Kristina
Hanspers
2016-
2016

32. David Gordon
(Nevan
Krogan)
UCSF HIV host-pathogen genetic
interaction networks
Alex Pico,
Scooter Morris
2016-
2016
Nicole Stone
(Deepak
Srivastava)
Gladstone
Institutes
Network and pathway
analysis of cardiac
reprogramming
Alex Pico,
Kristina
Hanspers
2016-
2016
Gan Li Gladstone
Institutes
Network analysis of perturbed
cell model for Alzheimer’s
disease
Alex Pico 2016-
2016
Katerina
Akassoglou
Gladstone
Institutes
Treatment effects on
Alzheimer’s disease networks
and TYROBP pathway
Alex Pico,
Kristina
Hanspers
2016-
2016
Elizabeth
Mertsching
aTyr Pharma Pathway analysis of RNA
sequencing
Aaron Chang 2016-
2016
Commercial
sponsor
Devesh
Khandelwal
University of
Delhi, India
SBGN-ML and SBML to
Escher converter
Zachary King,
Alex Pico
2016-
2016
Google
Supun
Arunoda
University of
Moratuwa, Sri
Lanka
PCA and t-DSNE in
clusterMaker2
Scooter Morris 2016-
2016
Google
Istemi Bahceci Bilkent
University,
Turkey
Visualizing genomic
alterations in TCGA cancer
pathways in cBioPortal
Ugur Dogrusoz,
Chris Sander
2016-
2016
Google
Metin Can
Siper
Bilkent
University
Computer
Science
Department,
Ankara,
Turkey
Improving Cytoscape.js
Based Viewer for SBGN
Process Description
Diagrams with Better Layout
and Advanced Complexity
Management Operations
Augustin Luna,
Uğur Doğrusöz,
Onur Sümer
2016-
2016
Google
Julia
Gustavsen
University of
British
Columbia
RCy3 for network
manipulation using
Cytoscape
Augustin Luna 2016-
2016
Google
Ivan Bestvina University of
Zagreb,
Croatia
Multithread Centiscape Giovanni
Scardoni, Alex
Pico
2016-
2016
Google
Hovakim
Grabski
Russian
Armenian
University,
Armenia
Deviser for SBML libraries Frank
Bergmann, Alex
Pico
2016-
2016
Google
Kaito Ii Keio University
Graduate
School of
Science and
Technology,
Japan
Interconvertable layout
program for CellDesigner
Akira
Funahashi, Alex
Pico
2016-
2016
Google
Mridul Seth Birla Institute
of Technology
and Sciences,
India
Cytoscape file import into
GraphSpace
TM Murali, Alex
Pico
2016-
2016
Google

33. Roman
Schulte
Eberhard Karls
University,
Germany
JSBML validation system Andreas
Drager, Alex
Pico
2016-
2016
Google
Ashish Tiwari Arizona State
University
Cytoscape command line
scripting enhancements
Scooter Morris 2016-
2016
Google
Tramy Nguyen University of
Utah
SBML and BioPAX
coversions
Mike Hucka,
Alex Pico
2016-
2016
Google
Joseph Stahl Vanderbilt
University
Cytoscape.js interactive
tutorials
Max Franz 2016-
2016
Google
William Miles McMaster
University,
Canada
TOR-IBIN web interface
development
Mohammed
Helmy
2016-
2016
Google
Zhaoyuan Zoe
Xi
UCLA Cytoscape.js clustering
algorithms
Mike Kucera 2016-
2016
Google
Michael
Rosenberg
Agilent
Technologies
Pathway analysis of human
toxome
Alex Pico 2016-
2016
Alberto Ocaña Albacete
University
Hospital,
Albacete,
Spain
Identification and optimization
of targeted drug combinations
in breast cancer
Gary Bader/
Veronique
Voisin
2015-
2016
26314846
Chi-Hua Chen University of
California, San
Diego
Barabasi disease-disease
interactome analysis for
schizophrenia vs bipolar
GWAS genes
Aaron Chang 2015-
2016
NIH 1
R01MH100351
Danielle
Swany
(Krogan)
UCSF Cytoscape and Mass Spec
Workshops
Alex Pico 2015-
2016

34. Douglas
Levine
Memorial
Sloan-
Kettering
Cancer Center
Pathway and Network
Analysis of Endometrial
Cancer
Jianjiong Gao 2015-
2016
TCGA, NCI 23636398
Jill Mesirov Broad Institute GenomeSpace, Broad
Integrative Genomics Viewer
Jianjiong Gao 2015-
2016
John DIck Ontario
Cancer
Institute,
Toronto,
Canada
ITGb7+ and ITGb7–
hematopoietic stem cells.
Gary Bader/
Veronique
Voisin
2015-
2016
Kristin Hope McMaster
University,
Hamilton,
Canada
Pathway and Network
analysis of RNAseq data
using Cytoscape/
EnrichmentMap: comparison
between MSI2
overepxression and normal
cord blood samples.
Gary Bader/
Veronique
Voisin
2015-
2016
27121842
Peter Dirks Hospital for
Sick Children,
Toronto,
Canada
Standard training in one-on-
one session for gene-set
enrichment using
Cytoscape/EnrichmentMap ,
ASCL1 knockout in
glioblastoma
Gary Bader/
Veronique
Voisin
2015-
2016
Sandy
Williams, PhD
Gladstone
Institutes
Co-author networks and
metrics
Alex Pico 2015-
2016
Cynthia
Guidos
Hospital for
Sick Children,
Toronto,
Canada
IL-7 coordinates proliferation,
differentiation and Tcra
recombination during
thymocyte β-selection.
Gary Bader/
Veronique
Voisin
2015-
2016
25729925
John DIck Ontario
Cancer
Institute,
Toronto,
Canada
assessing expression levels
of erUPR and translation
initiation genes in normal and
leukemic stem cells
Gary Bader/
Veronique
Voisin
2015-
2016
John Dick Ontario
Cancer
Institute,
Toronto,
Canada
Normal and cancer
hematopoietic stem cells:
miR-126
Gary Bader/
Veronique
Voisin
2015-
2016
27300437,
27070706
John DIck Ontario
Cancer
Institute,
Toronto,
Canada
Pathways and Processes
active in leukemic stem
cells(LSC) of Acute Myeloid
Leukemia(AML) using label
free protein mass
spectrometry
Gary Bader/
Veronique
Voisin
2015-
2016

35. John Dick Ontario
Cancer
Institute,
Toronto ,
Canada
Protein expression in CD34+
cells overexpressing mir125a
Gary Bader/
Veronique
Voisin
2015-
2016
27424784
Laurie Ailles Ontario
Cancer
Institute (OCI),
Toronto,
Canada
Crosstalk between CAFs
(cancer associated
fibroblasts) and epithelial
cancer cells in the Head and
Neck cancer.
Gary Bader/
Veronique
Voisin
2015-
2016
Theodore J.
Brown
Lunenfeld-
Tanenbaum
Research
Institute,
Toronto,
Canada
Function of the fallopian tube
and ovulation in the
predisposition to ovarian
cancer
Gary Bader/
Veronique
Voisin
2015-
2016
26039994
Aaron D
Schimmer
The Princess
Margaret
Hospital, The
Ontario
Cancer
Institute,
University
Health
Network
Metabolic adaptation to
chronic inhibition of
mitochondrial protein
synthesis in acute myeloid
leukemia cells.
Gary Bader,
Veronique
Voisin
2013-
2016
NIH 23520503
Claudia C Dos
Santos
Keenan
Research
Centre of the
Li Ka Shing
Knowledge
Institute of St.
Michael's
Hospital
Acute lung injury Gary Bader,
Veronique
Voisin
2013-
2016
Canadian
Institutes of
Health
Research
Jaime O.
Claudio, Jean
Wang, John E.
Dick
Ontario
Cancer
Institute
Development of Highly Active
Anti-Leukemia Stem Cell
Therapy
Gary Bader,
Veronique
Voisin
2013-
2016
Ontario
Institute for
Cancer
Research
Jayne Danska SickKids microbiome and alterations in
gene expression
Gary Bader,
Veronique
Voisin
2013-
2016
Canadian
Institutes for
Health
Research,
Juvenile
Diabetes
Research
Foundation
Nadeem
Moghal
Ontario
Cancer
Institute
Stem/progenitor cell biology
in human lung.
Gary Bader,
Veronique
Voisin
2013-
2016
Canadian
Institutes of
Health
Research

36. Peter Dirks Hospital for
Sick Children
Isolation and characterization
of a cancer stem cell from
human brain tumours.
Gary Bader,
Veronique
Voisin
2013-
2016
Ontario
Institute for
Cancer
Research
25561528
Peter Dirks Hospital for
Sick Children
Pathway and Network
analysis of RNAseq data
using Cytoscape/
EnrichmentMap: Role of
dopamine D4 receptor in
glioblastoma stem cells
Gary Bader,
Veronique
Voisin
2013-
2016
Ontario
Institute for
Cancer
Research
27300435
Charles Perou University of
North Carolina
Pathway and Network
Analysis of Breast Cancer
Chris Sander 2012-
2016
TCGA
Genome Data
Analysis
Center, NCI
23000897,
24096568,
26451490
Mathew
Meyerson
Harvard
University
Pathway and Network
Analysis of Lung Cancer
Chris Sander,
Debbie Bemis
2012-
2016
TCGA
Genome Data
Analysis
Center, NCI
22960745
Eldad
Zacksenhaus
Toronto
General
Research
Institute,
Canada
Breast cancer Gary Bader 2011-
2016
Ontario
Institute for
Cancer Stem
Cell Research
22460789;
25330770;
27571409
Jayne
Danska,
Cynthia
Guidos
University of
Toronto,
Canada
Pathway and network
analysis of mouse models of
leukemia
Daniele Merico,
Gary Bader
2011-
2016
Ontario
Institute for
Cancer
Research
John Dick Ontario
Cancer
Institute
Normal and cancer
hematopoietic stem cells
Gary Bader 2011-
2016
Ontario
Institute for
Cancer Stem
Cell Research
23142521
Katherine
Siminovitch
University of
Toronto,
Canada
Clinical genomics of human
genetic diseases
Ruth Isserlin,
Gary Bader
2011-
2016
Margaret
Wrensch
University of
California San
Francisco
Genetic and Molecular
Epidemiology of Adult Glioma
Alexander Pico 2011-
2016
NIH 24908248,
22922872,
23733245,
23361564
Michael Taylor University of
Toronto,
Canada
Pathway and network
analysis of pediatric brain
tumours
Ruth Isserlin,
Gary Bader
2011-
2016
Genome
Canada
22832581,
21840481,
20393554,
24553142
Nevan Krogan UCSF Evolution of viral-human
protein complexes
Trey Ideker,
Scooter Morris,
Alex Pico
2011-
2016
NIAD P01
AI091575,
NIGMS R01
GM098101
23273983,
22190034,
23242164,
22681890,
22252388,
21127252