Download 4 - Indiana University

MURI Project Proposal Form
Section I: Proposal Cover Page
Date of submission:
08/22/2012
Proposed project title: Integrative Pathway Modeling for Pancreatic Cancer Drug
Assessment and Discovery (Phase II)
Principle Mentor
Name: Jake Chen
Phone number: 317-278-7604
Department: Bioinformatics
Title: Associate Professor
Email: [email protected]
School: Informatics
Co-mentor
Name: Xiaogang Wu
Phone number: 317-274-7542
Department: Bioinformatics
Title: Assistant Scientist
Email: [email protected]
School: Informatics
Co-mentor
Name:
Phone number:
Department:
Title:
Email:
School:
Please note that preference will be given to projects that include mentors from multiple
disciplines.
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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Section II: Student Request Page
Total number of students requested:
4
(Note: The total number of students must exceed by two the number of mentors)
Total Number of freshmen and/or sophomores to be recruited:
depending on qualification
(math/computer/engineering students preferred)
(Note: Preference will be given to projects that include at least one freshman and/or sophomore)
Disciplines or majors of students (preference will be given to projects that include at least two
disciplines or majors): Biology, Chemistry, Computer Science, mathematics, Engineering
Skills expected from students: Computer Science - excel, sql, matlab, linux, php, java;
Biology - cellular biology, molecular biology, neurology; Math - statistics, graph theory
Names of students you request to work on this project.
(Mentors are invited to recommend students that they would prefer to work on the proposed
project. Please provide an email address and a rationale; for example, a student may have an
essential skill, may already be working on a similar project, or may be intending to apply to
graduate school to pursue the same area of research.)
The Center for Research and Learning will consider the students requested below, but cannot
guarantee placement of specific students on teams.
Name of Student:
Student’s Email:
Rationale:
1) Sara Ibrahim
[email protected]
Sara worked in Dr. Chen’s lab for
the 2011-2012 MURI project and is interested in furthering her work based on systems
pharmacology and performing pharmacogenomics data analysis.
2) Biology/Chemistry (TBN)
Build pancreatic cancer -specific
pathway/network models through integrating drug-protein interactions and pathways containing
crucial pancreatic cancer-associated genes/proteins and drug targets.
3) Computer Science/ Engineering/Math (TBN)
_____________________ Build,
compare and integrate drug-drug similarity networks from different data types. Also help
improve an online software platform to retrieve, parse, and annotate drug-disease-protein
relationships.
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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4) Computer Science/Math (TBN)
Develop computational
algorithms to evaluate therapeutic effects of potential drugs/drug combinations through the
integrated pancreatic cancer-specific pathway/network models.
Section III: Body of Proposal
(A maximum of 5 pages is allowed for answers to questions 1-11.)
1) Please list the research objectives for the proposed project.
This MURI 2012-2013 project is a Phase II stage project, continued with the successful implementation of
the Phase I stage MURI 2012 summer project – “Integrative Pathway Modeling for Pancreatic Cancer
Drug Assessment and Discovery”. The objective of this project is to continue developing, testing and
validating a computational platform to screen potential drugs and apply this in-silico platform to assess
the therapeutic efficacy of these drugs/drug panels specific for pancreatic cancer. To achieve this
objective, we have the following specific aims:

Aim 1: Curate drug-protein directionality specific for pancreatic cancer based on
CMaps/PubMed, and integrate pancreatic cancer drug specific pathway models. The protein
list is obtained from different credible databases such as CMaps, OMIM and GAD. In the PPI
network, a protein-protein interaction is represented by a directional edge with a specific arrow
head to indicate the interaction: either stimulation or inhibition. In this study, a pathway is created
with the appearance of a drug. It is an integration of all paths in the network starting by all drug’s
targets.

Aim 2: Develop and test therapeutic effect evaluation algorithms based on pathway/network
models and signal flow theory. In this study, we only consider the effect of the drug on proteins
along the pathway for the drug evaluation. We consider the drug and the proteins as stations,
which can transmit the signal to other station(s) through signal channels. Two types of signal are
stimulation (+) and inhibition (-). The drug serves as the source station, which can only send the
signal to its target proteins.

Aim 3: Build, compare and integrate drug-drug similarity networks from different data types –
drug chemical structures, shared drug targets, drug side effects and drug ontology. We will collect
information for these criteria from popular databases such as PubChem, DrugBank and
MetaDrug. The drug similarity network contributes the validation for our framework, based on a
hypothesis that two drugs having high similarity should have similar therapeutic effects on the
diseases.
2) Please identify the specific research question(s) that your proposed project will address.
Hypothesis: For a patient with a complex disease (e.g. cancers), usually caused by multiple genes
interacting with each other, “ideal” drugs should cure the disease by modulating the patient’s gene
expression profile close to those in healthy people at pathway level. So for those statistically overexpressed genes in disease-related pathways, drugs should be able to inhibit their expression level to the
normal range. Similarly, for those statistically under-expressed genes in disease-related pathways, drugs
should be able to activate their expression level to the normal range. In this way, these drugs can reverse
the gene expressions from disease status to the normal range thus maintaining cellular function as a
normal cell at pathway level.
We propose to significantly advance our knowledge on pathway-level functional relationships based on
the concept of computational connectivity maps, called “CMaps” [1, 2]. Since we focus on pathway level
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here, there are two major questions – which pathways are crucial for pancreatic cancer, and how will
drugs affect genes/proteins in these crucial pathways?
3) Please describe the significance of the research.

Our method incorporates both biological information and traditional graph based model ranking
systems, which provide more biological context while allowing us to utilize traditional graph
methods.

Our method will allow research to render curated biological information more effectively.
Furthermore, The PET algorithm will be able to use the topology underlying our PEN without
relying solely on the topological features for ranking drugs. Instead, the PET algorithm will utilize
both topological information and biological information to derive its results.

Our method is ideal for implementing personalized medicine, which not only considers the
disease involved but will also use gene expression data from individual patients.

Our method is more accurate in predicting drugs for individual patients. For example, if there are
two different patients, there might be different drugs proposed due to the fact that the patients
might have different gene expression profiles.
4) Why does this proposal offer a good opportunity for undergraduate researchers to gain
substantive research skills?
Biology and math students will learn how to apply their knowledge into bioinformatics research, while
computer science students will learn how to implement bioinformatics tools based on hypothesis-driven
systems biology. Both of them will learn how important multidisciplinary research is in the field of systems
biology and personalized medicine. Most importantly, the students will develop essential skills to prepare
for professional or graduate studies. This project also provides a great opportunity to publicize the
significance of translational science to undergraduate students, and will help them decide the future study
goals and career goals.
5) Please describe the research methodology and the specific tasks that students and mentors
will undertake.
Traditional treatment strategy development for diseases involves the identification of target proteins
related to disease states, and the interference of these proteins with drug molecules. Computational drug
discovery and virtual screening from thousands of chemical compounds have accelerated this process
[3]. Some of these methods try to discover a “magic bullet” for a particular disease by identifying single
drug target from genomic studies and then designing a spectacular compound that can bind to this target
[4]. These conventional “One gene, One drug, One disease” oriented methods show their efficiency for
several simple diseases, while failing to predict drugs for complex diseases, such as cancers [5].
Pathway modeling approaches may improve the traditional way a lot. The primary goal of emerging
pathway modeling approaches is to determine a specific drug’s effect on metabolism, its toxicity, and its
pharmacokinetics. However, most of pathway modeling approaches only focus on the structural formula
of the drug [6]. Although focusing on the structural formula of the drug is an effective way of determining a
drug’s effect on a protein, there is room for improvement by utilizing the concept of network
pharmacology [7] or network medicine [8].
In post-genome biology, molecular connectivity maps have been proposed to establish comprehensive
knowledge links between molecules of interest in a given biological context [9]. Molecular connectivity
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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maps between small molecule drugs and genes in a disease-specific context can be particularly valuable
because they allow researchers to evaluate drugs against each other using their unique gene/proteindrug association profiles. The functional approach to drug comparisons helps researchers gain global
perspectives on both the toxicological profiles and therapeutic profiles of candidate drugs. The
Connectivity Maps (CMaps) web server [2] is an online bioinformatics resource that provides biologists
with potential relationships between drugs and genes in specific disease contexts. A new insight to
assess overall drug efficacy profiles can be provided by using CMaps to identify disease relevant proteins
and drugs and then constructing unified pathway models from the relevant proteins and drugs.
In this project, we will investigate the feasibility of combining the pathway modeling approach with the
CMaps method to identify and rank drug compounds with the best overall drug efficacy profile, using
pancreatic cancer as a case study. We plan to use our current C-Map webserver [2], global human
annotated and predicted protein interaction (HAPPI) database [10] and human pathway database (HPD)
[11] to construct our unified pathway/network model. Pancreatic cancer specific proteins and drugs will be
identified on the CMaps webserver and protein interactions will be retrieved from both existing pathways
and protein interactions.
Figure 1. A workflow for developing Pharmacological Effect Network (PEN) models and Pharmacological
Effect on Target (PET) evaluating/ranking algorithms
A workflow for developing pharmacological effect network (PEN) models and to implement
pharmacological effect on target (PET) evaluating/ranking algorithms is shown in Figure 1, which is
designed to identify chemical compounds/drugs which can reverse the expression direction of those
critical genes related to the disease states. We plan on building an integrated pancreatic cancer specific
pathway/network model consisting of important pancreatic cancer drugs, genes and proteins. The
importance of drugs and proteins can be determined by using the CMaps webserver. The CMaps
webserver uses disease name as the input and applies network mining and text mining to determine
disease related proteins and drugs with support of a set of PubMed abstract for each drug-protein
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relation. Important proteins can then be used as queries on the Human Pathway Database to determine
top ranking pathways. Each top pathway should then be annotated and mapped to form an integrated
pathway.

Task 1: Build an integrated pancreatic cancer specific pathway/network model by searching
pancreatic cancer related pathways online and integrating them into a PEN model with
directionality information. One biology student, mentored by a graduate student - Hui Huang, will
learn and implement biological pathway/network modeling tools.

Task 2: Develop signal-flow based algorithms to evaluate therapeutic effects of candidate
pancreatic cancer drugs/drug panels from the PEN model by applying graph theory and matrix
theory to calculating PET score for each drug or drug panel. One math/computer science student
- TBN, mentored by Dr. Xiaogang Wu, will learn and apply graph theory into translational
bioinformatics, especially in network pharmacology and pharmacogenomics.

Task 3: Build, compare and integrate drug-drug similarity networks from different data types –
drug chemical structures, shared drug targets, drug side effects and drug ontology. One computer
science student - TBN, mentored by Dr. Jake Chen (with the help of a graduate student - Hui
Huang), will learn and implement online databases, data retrieval and visualization techniques.

Task 4: Validate the pathway-based drug evaluating algorithms by using pancreatic cancer
related microarray datasets mapped onto the PEN model. One biology student - Sara Ibrahim,
mentored by Dr. Jake Chen, will learn and implement classic bioinformatics software tools (e.g. R
Bioconductor package).
6) What plan has been designed to ensure effective communication with all co-mentors and
undergraduate researchers on the MURI team?
To ensure effective communication between all mentors, graduate and undergraduate researchers on the
research team, a mentor will be present in lab whenever a student comes to work. Every week, we plan
to use Skype and IU webinar (http://breeze.iu.edu/sysnet) system to discuss project progress. Also, we
will be using online collaboration software such as Google groups, Google doc, and Google site to share
and update documentation related to our project. In fact, we have been actively using these collaboration
software tools since 2007 in our group, to engage collaborating students from China, India, and
elsewhere in the United States. The aim of these meetings is to discuss the work the undergraduate
researchers have performed throughout the week and to discuss future plans for the upcoming week.
These meetings will also provide an opportunity for students to understand the progress of the whole
project, change thoughts with other students, and see how the different disciplines are intertwined.
Furthermore, these online communication tools can be used without any mentors at anytime and
anywhere, which can encourage students discuss research more freely, enhance the daily connection
between students, and even make them as best friends having common research interests.
7) What measureable outcomes and benefits do you anticipate this research will provide?

An integrated pathway/network model specific for pancreatic cancer

Pathway-based algorithms to evaluate therapeutic effects of candidate pancreatic cancer drugs

Drug-drug similarity network models specific for pancreatic cancer

Network-based approaches for microarray analysis
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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
Function-enhanced CMaps platform for retrieving drug-gene/protein directionality information and
pathway data management

Research publications and improvement for funding competitiveness
8) What is the timeline for the major tasks associated with this proposal?
Tasks
Participants
Timeline
Aim 1
Task 1
Dr. Jake Chen (PM), Dr. Xiaogang Wu (Co-M), Hui Huang
(graduate-M), and one biology student
Task 1
[Oct 2012 – Feb 2013]
Aim 2
Task 2, 4
Dr. Jake Chen (PM), Dr. Xiaogang Wu (Co-M), Hui Huang
(graduate-M), one biology student (Sara Ibrahim) and one
math/computer science student
Task 2
[Oct 2012 – Dec 2012]
Task 4
[Jan 2013 – Apr 2013]
Aim 3
Task 3
Dr. Jake Chen (PM), Dr. Xiaogang Wu (Co-M), Hui Huang
(graduate-M), and one computer science student
Task 3
[Oct 2012 – Mar 2013]
9) Please provide a rationale for your budget request. (NOTE: The maximum budget allowance is
$2,000 for equipment and/or supplies needed for the research team. Generally speaking,
expenditures for computers and/or travel are not approved by the review committee at this
time due to financial constraints.)

Hard Drives and Hardware Accessories for server storage/network (not PC)
$1000

Publication in Peer-reviewed Open Access Journals (e.g., BMC series)
$1000
10) Please describe your plan for sustaining your research beyond the funding that MURI is able
to provide. (For example, please list other external grants that have been or will be submitted
as a follow-up to your MURI funding.)
We will use the results and findings from the MURI project as a preliminary study to apply grants from
related National Institute of Health (NIH) and National Science Foundation (NSF) program, including:
Exploratory Innovations in Biomedical Computational Science and Technology (NIH R21, PAR-09-219),
Innovations in Biomedical Computational Science and Technology (NIH R01, PAR-09-218), Innovations
in Biomedical Computational Science and Technology Initiative (NIH SBIR/STTR R41/R42, PAR-09-221),
and Advances in Biological Informatics (ABI, NSF 10-567).
11) Please identify any areas relevant to risk management.
No risk on the following issues:
All university policies with respect to research must be followed. The usual risk management
assurances must be provided where appropriate (animal use, radiation safety, DNA, human
subjects protocols) in accordance with the university policies. No funds may be released without
risk-management assurances, where needed. Project proposals without required compliance
approvals will be reviewed but the funds will not be released until approval is given by the
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
7
university. Further information on risk management is available from
http://researchadmin.iu.edu/cs.html
Please check any risk assurances that apply to this proposal:
Animals (IACUC Study #): _________________
Human Subjects (IRB Study #): ____________________
r-DNA (IBC Study #): _____________________
Human Pathogens, Blood, Fluids, or Tissues must be identified if used: ______
Radiation : ______
Other : ______
12) The center for Research and Learning generally shares the text of funded proposals on the
web so that prospective students can learn about available MURI projects. Please let us know
if it is OK with you to post your proposal on the CRL MURI webpage by checking one of the
following answers:
YES
NO
Section IV: References/Bibliography (insert 1-2 pages as needed)
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
Lamb, J., et al., The Connectivity Map: using gene-expression signatures to connect small
molecules, genes, and disease. Science 2006, 313, 1929-1935.
Li, J., X. Zhu, and J.Y. Chen, Building disease-specific drug-protein connectivity maps from
molecular interaction networks and PubMed abstracts. PLoS computational biology 2009, 5,
e1000450.
Mestres, J., Computational chemogenomics approaches to systematic knowledge-based drug
discovery. Current opinion in drug discovery & development 2004, 7, 304-13.
Roses, A.D., Pharmacogenetics and the practice of medicine. Nature 2000, 405, 857-65.
Yildirim, M.A., et al., Drug-target network. Nature biotechnology 2007, 25, 1119-26.
Bugrim, A., T. Nikolskaya, and Y. Nikolsky, Early prediction of drug metabolism and toxicity:
systems biology approach and modeling. Drug discovery today 2004, 9, 127-35.
Hopkins, A.L., Network pharmacology: the next paradigm in drug discovery. Nature chemical
biology 2008, 4, 682-690.
Barabási, A.L., N. Gulbahce, and J. Loscalzo, Network medicine: a network-based approach to
human disease. Nature Reviews Genetics 2011, 12, 56-68.
Lamb, J., et al., The Connectivity Map: using gene-expression signatures to connect small
molecules, genes, and disease. Science 2006, 313, 1929.
Chen, J.Y., S. Mamidipalli, and T. Huan, HAPPI: an online database of comprehensive human
annotated and predicted protein interactions. BMC Genomics 2009, 10 Suppl 1, S16.
Chowbina, S.R., et al., HPD: an online integrated human pathway database enabling systems
biology studies. BMC Bioinformatics 2009, 10 Suppl 11, S5.
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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Section V: CVs/Resumes (insert 2 pages per mentor for a maximum of 6 pages)
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
9
POSITION TITLE
Principal Mentor (PM)
Chen, Jake Yue
Associate Professor of Informatics
Director, Indiana Center of Systems
Biology and Personalized Medicine
eRA COMMONS USER NAME
JAKECHEN
EDUCATION/TRAINING
INSTITUTION AND LOCATION
DEGREE
YEAR
FIELD OF STUDY
Peking University, Beijing, China
University of Minnesota, Minneapolis
B.S.
M.S.
1995
1997
University of Minnesota, Minneapolis
Ph.D.
2001
Biochemistry and Molecular
Biology
Computer Science and
Engineering
Computer Science and
Engineering
Positions and Employment History
2010Associate Professor of Informatics, Indiana University School of Informatics,
Indianapolis, IN
2010Associate Professor of Computer Science (joint appointment), Department of
Computer and Information Science, Purdue University School of Science,
Indianapolis, IN
2007Funding Director, Indiana Center for Systems Biology and Personalized
Medicine, Indianapolis, IN
2004-2010 Assistant Professor of Informatics, Indiana University School of Informatics,
Indianapolis, IN
2004-2010 Assistant Professor of Computer Science (joint appointment), Department of
Computer and Information Science, Purdue University School of Science,
Indianapolis, IN
Selected recent book and journal publications
1. Xiaogang Wu, Hui Huang, Madhankumar Sonachalam, Sina Reinhard, Jeffrey Shen, Ragini
Pandey, and Jake Y. Chen (2012) Reordering Based Integrative Expression Profiling for
Microarray Classification. BMC Bioinformatics, Vol. 13, Suppl. 2, S1.
2. Liang-Chin Huang, Xiaogang Wu, and Jake Y. Chen (2011) Predicting Adverse Side Effects
of Drugs. BMC Genomics, Vol. 12, Suppl. 5, S11.
3. Jiliang Li, Fan Zhang, and Jake Y. Chen (2011) An Integrated Proteomics Analysis of Bone
Tissues in Response to Mechanical Stimulation. BMC Systems Biology, Vol. 5, Suppl. 3, S7.
4. Fengjun Li, Xukai Zou, Peng Liu, and Jake Y. Chen (2011) New Threats to Health Data
Privacy. BMC Bioinformatics, Vol. 12, Suppl. 12, S7.
5. Fan Zhang and Jake Y. Chen (2011) HOMER: a human organ-specific molecular electronic
repository, BMC Bioinformatics, Vol. 12, , Suppl. 5, S4.
6. Sudhir Chowbina, Youping Deng, Junmei Ai, Xiaogang Wu, Xin Guan, Mitchell S. Wilbanks,
Barbara Lynn Escalon, Sharon A. Meyer, Edward J. Perkins, and Jake Y. Chen (2010)
Dose Responsive Pathway-Connected Networks in Rat Liver Regulated by 2,4DNT. BMC
Genomics, Vol. 11, Supplement 3, S4.
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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7. Fan Zhang and Jake Y. Chen (2010) A Systems Biology Approach to Discovering and
Validating Breast Cancer Protein Biomarkers in Human Plasma. BMC Genomics, Vol. 11,
Supplement 2, S12.
8. Ao Zhou, Fan Zhang, and Jake Y. Chen (2010) PEPPI: A Peptidomic Database of Human
Protein Isoforms for Proteomics Experiments. BMC Bioinformatics, Vol. 11, Supplement 6,
S7.
9. Jiao Li, Xiaoyan Zhu, and Jake Y. Chen (2009) Building Disease-specific Drug-Protein
Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts. PLoS
Computational Biology, 5(7): e1000450.
10. Sudhir R. Chowbina, Xiaogang Wu, Fan Zhang, Peter M. Li, Ragini Pandey, Harini N.
Kasamsetty, and Jake Y. Chen (2009) HPD: An Online Integrated Human Pathway
Database Enabling Systems Biology Studies. BMC Bioinformatics, Vol. 10, Supplement 11,
S5.
11. Jake Y. Chen, SudhaRani Mamidipalli, and Tianxiao Huan (2009) HAPPI: an Online
Database of Comprehensive Human Annotated and Predicted Protein Interactions. BMC
Genomics, Vol. 10, Supplement 1, S16
12. Huajun Chen, Li Ding, Zhaohui Wu, Tong Yu, Lavanya Dhanapalan, and Jake Y. Chen
(2009) Semantic Graph Mining for Biomedical Network Analysis: an Overview. Briefings in
Bioinformatics, Vol. 10, No. 2, pp. 177-192.
13. Sudipto Saha, Scott H. Harrison, and Jake Y. Chen (2009) Dissecting the Human Plasma
Proteome and Inflammatory Response Biomarkers. Proteomics, Vol. 9, No. 2, pp. 470-484.
14. Tianxiao Huan, Andrey Sivachenko, Scott H. Harrison, and Jake Y. Chen (2008)
ProteoLens: a Visual Analytic Tool for Multi-scale Database-driven Biological Network Data
Mining. BMC Bioinformatics, Vol. 9, S5, pp. 1-13.
15. Sudipto Saha, Scott H. Harrison, Changyu Shen, Haixu Tang, Predrag Radivojac, Randy J.
Arnold, Xiang Zhang, and Jake Y. Chen (2008) HIP2: An Online Database of Human
Plasma Proteins from Healthy Individuals. BMC Medical Genomics, 2008, Vol. 1, 12.
Edited Journal Special Issues
16. Stefano Lonardi and Jake Y. Chen, ed. (2009) Special Issue on Data Mining in
Bioinformatics (BIOKDD 2008), IEEE/ACM Transactions on Computational Biology and
Bioinformatics, Vol. 6, No. 4.
17. Stefano Lonardi and Jake Y. Chen, ed. (2008) Special Issue on Data Mining in
Bioinformatics (BIOKDD 2007), Journal of Bioinformatics and Computational Biology, Vol. 6,
No. 6.
18. Amandeep S. Sidhu, Tharam S. Dillon, Elizabeth Chang and Jake Y. Chen, ed. (2007)
Special Issue on Ontologies for Bioinformatics, International Journal of Bioinformatics
Research and Applications, Vol. 3, No. 3.
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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Co-Mentor (Co-M)
Wu, Xiaogang
eRA COMMONS USER NAME
POSITION TITLE
Visting Research Scientist, Indiana University
school of Informatics, Indianapolis
XIAOGANG
EDUCATION/TRAINING
INSTITUTION AND LOCATION
DEGREE
YEAR
FIELD OF STUDY
Huazhong University of Science and
Technology, Wuhan, China
B.S.
1996
Electronic and
Information Engineering
Huazhong University of Science and
Technology, Wuhan, China
M.S.
1999
Pattern Recognition and
Artificial Intelligence
Huazhong University of Science and
Technology, Wuhan, China
Ph.D.
2005
Control Science and
Engineering
Positions and Employment History
2009Visiting Research Scientist, Indiana Center for Systems Biology and
Personalized Medicine, Indianapolis, IN
2007-2009 Postdoctoral Fellow of Bioinformatics, Indiana University School of Informatics,
Indianapolis, IN
2006-2010 Associate Professor, Institute for Pattern Recognition and Artificial Intelligence,
Huazhong University of Science and Technology, China
Professional Experience
2009Associated Editor, Frontiers in Systems Biology
Selected Honors and Awards
2007
Hubei Provincial Technical Invention Award, China
2006
Distinguished Dissertation Award, Huazhong University of Science and
Technology, China
2004
Hubei Provincial Technical Achievement Award, China
2003
National Ministry of Education, Technical Achievement Award, China
2002
Hubei Provincial Technical Achievement Award, China
1999
Distinguished Graduate, Huazhong University of Science and Technology, China
1998
Merit Graduate, Huazhong University of Science and Technology, China
1996
Nominee for American Mathematics Modeling Competition Award, China
1996
Hubei Provincial Research Achievement Award for Graduates, China
Selected Peer-reviewed Publications
Journal Paper (15 publications related to this project)
1. Xiaogang Wu, Hui Huang, Madhankumar Sonachalam, Sina Reinhard, Jeffrey Shen,
Ragini Pandey, Jake Y. Chen: Reordering based integrative expression profiling for
microarray classification. BMC Bioinformatics 2012, 13(Supp 2):S1.
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2. Liang-Chin Huang, Xiaogang Wu, Jake Y. Chen: Predicting Adverse Side Effects of Drugs.
BMC Genomics 2011, 12(Supp 5):S11.
3. Sudhir Chowbina, Youping Deng, Junmei Ai, Xiaogang Wu, Xin Guan, Mitchell S. Wilbanks,
Barbara Lynn Escalon, Sharon A. Meyer, Edward J. Perkins, and Jake Y. Chen: Dose
Responsive Pathway-Connected Networks in Rat Liver Regulated by 2,4DNT. BMC
Genomics, 2010, 11(Supp 3):S4.
4. Tianxiao Huan, Xiaogang Wu*, Zengliang Bai, and Jake Y. Chen (2011) Seed-weighted
Random Walk Ranking for Cancer Biomarker Prioritization: a Case Study in Leukemia.
International Journal of Data Mining and Bioinformatics. (In Press) (*Equally-contributed
author)
5. Tianxiao Huan, Xiaogang Wu, and Jake Y. Chen: Systems Biology Visualization Tools for
Drug Target Discovery. Expert Opinion on Drug Discovery 2010, 5(5):425-439.
6. Xiaogang Wu, Tianxiao Huan, Ragini Pandey, Tianshu Zhou, and Jake Y. Chen: Finding
Fractal Patterns in Molecular Interaction Networks: a Case Study in Alzheimer's Disease.
International Journal of Computational Biology and Drug Design 2009, 2(3):340-52.
7. Sudhir R. Chowbina, Xiaogang Wu*, Fan Zhang, Peter M. Li, Ragini Pandey, Harini N.
Kasamsetty, and Jake Y. Chen: HPD: An Online Integrated Human Pathway Database
Enabling Systems Biology Studies. BMC Bioinformatic. 2009, 10(Supp 11):S5. (*Equallycontributed author)
8. Xiaogang Wu, and Zuxi Wang: Estimating parameters of chaotic systems under noiseinduced synchronization. Chaos, Solitons & Fractal 2009,39:689–696.
9. Xiaogang Wu, and Zuxi Wang: Estimating parameters of chaotic systems synchronized by
external driving signal. Chaos, Solitons & Fractals 2007, 33:588-594.
10. Hanping Hu, Xiaogang Wu*, and Zuxi Wang: Synchronizing chaotic map from two-valued
symbolic sequences. Chaos, Solitons & Fractals 2005, 24:1059-1064. (*Communication
author)
11. Xiaogang Wu, Hanping Hu, and Baoliang Zhang: Analyzing and improving a chaotic
encryption method. Chaos, Solitons & Fractals 2004, 22:367-373.
12. Xiaogang Wu, Hanping Hu, and Baoliang Zhang: Parameter estimation only from the
symbolic sequences generated by chaos system. Chaos, Solitons & Fractals 2004, 22:359366.
13. Ling Liu, Xiaogang Wu*, and Hanping Hu: Estimating system parameters of Chua's circuit
from synchronizing signal. Physics Letters A 2004, 324:36-41. (*Communication author)
14. Hanping Hu, Shuanghong Liu, Zuxi Wang, and Xiaogang Wu: A chaotic poly-phase
pseudorandom sequence, Acta Mathematiea Scientia 2004, 2: 123-128.
15. Baoliang Zhang, Hanping Hu, Xiaogang Wu: Security enhanced to GSI: An integrated
framework with a mechanism. Lecture Notes in Computer Science 2004, 3252:506-513.
Book Chapter
1. Xiaogang Wu and Jake Y. Chen, Molecular Interaction Networks: Topological and
Functional Characterizations, in Automation in Genomics and Proteomics: An Engineering
Case-Based Approach. Wiley Publishing, May, 2009
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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Section VI: Support Letters (insert 1- 2 pages as needed)
Section VII: Appendix (Title of and information on the status and outcomes of the past Student
Multidisciplinary Research Team projects received by the Principal Mentor and/or any of the Co-Mentors
must be detailed here. Please insert 1 page summary per previous MURI project as needed according to
template below. Maximum - 5 pages.)
Title of Past MURI Project:
A Novel Approach to In Silico Drug Screening and Assessment for Alzheimer’s Disease
Date Awarded:
09/01/2011
Date Completed:
05/01/2012
Description:
We develop a novel approach based on integrative pathway modeling. Using Alzheimer’s disease (AD)
as an example, we identify and rank AD-related drugs/compounds with their overall drug-protein
“connectivity map” profile. This approach includes: 1) Retrieve AD-associated proteins through the CMaps
platform by using “Alzheimer’s disease” as a query term. 2) Retrieve AD-related pathways by using those
AD-associated proteins as input and searching in the Human Pathway Database (HPD) and the PubMed.
3) Integrate the AD-related pathways into unified pathway models, from which we categorize the
pharmaceutical effects of candidate drugs on all AD-associated proteins as either “therapeutic” or “toxic”
4) Transform the integrated pathways into network models and rank drugs based on the network
topological features of drug targets, drug-affecting genes/proteins, and curated AD-associated proteins.
Outcomes:
Poster presentations:
Title: Towards a Pathway Modeling Approach to Alzheimer’s Disease Drug Discovery
Date: 04/13/2012 (IUPUI Research Day 2012)
Students Involved: Sara Ibrahim, Don Capouch, and Sujay Chandorkar
Conference presentations:
Title: Predicting Drug Efficacy Based on the Integrated Breast Cancer Pathway Model
Date: 12/05/2011 (GENSIPS’11 Conference)
Students Involved: Sara Ibrahim, Marianne McKinzie
Publications:
Title: CMaps: A network pharmacology database with comprehensive disease-gene-drug
connectivity relationships
Date: 02/01/2012 (Submitted to BMC Genomics)
Students Involved: Sara Ibrahim
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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Title of Past MURI Project:
Computational Connectivity Maps (CMaps) Platform for Cancer Drug Discovery and Repurposing
Date Awarded:
09/01/2010
Date Completed:
05/01/2011
Description:
The goal of our project was to determine the efficacy of several Breast Cancer drugs. For this project, we
constructed an integrated Breast Cancer Pathway that included several important Breast Cancer
Proteins. Not only were several protein-protein interactions mapped on the pathway, but the drug-protein
interactions for 19 important Breast Cancer drugs were also portrayed on the pathway.
Outcomes:
Poster presentations:
Title: Predicting Drug Efficacy Based on the Connectivity Map and Integrated Breast Cancer Pathway
Date: 04/08/2011 (IUPUI Research Day 2011)
Students Involved: Sara Ibrahim, Marianne McKinzie, Everton Lima
Conference presentations:
Title: Evaluate Drug Effects on Gene Expression Profiles with Connectivity Maps
Date: 12/18/2010 (DMBD 2010 Conference)
Students Involved: Sara Ibrahim, Taiwo Ajumobi
Section VIII: Signature
Name and Signature of the Principal Mentor:
(typing in the full name suffices as signature for electronic copies)
Jake Y. Chen
8/22/2012
______________________________________________________________________
Name
Signature
Date
MURI Mentor’s Project Proposal Form, Updated: 11-28-2012
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