Proposal - Oncourse - Indiana University

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Oct 1, 2013 (3 years and 10 months ago)

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MURI
Mentor’s Project Proposal

Form, Updated: 11
-
28
-
2012


1

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

Title:

Associate Professor

Phone number:

317
-
278
-
7604

Email:

jakechen@iupui.edu

Department:

Bioinformatics

School:

Informatics


Co
-
mentor

Name:

Xiaogang Wu

Title:

Assistant

Scientist

Phone number:

317
-
274
-
7542

Email:

wu33@iupui.edu

Department:

Bioinformatics

School:

Informatics


Co
-
mentor

Name:

Title:

Phone number:

Email:

Department:

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


2


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/compute
r/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 le
ast two
disciplines or majors):

B
iology, 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 alrea
dy 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 Ibr
ahim

__
saibrahi@iupui.edu



Sara worked in Dr. Chen’s lab for
the 201
1
-
201
2

MURI project and is interested in furthering her work based on
systems

pharmacology and performing
pharmacogenomics data analysis.


2)

Biology/Chemistry

(TBN)





Build
p
ancreatic
c
ancer

-
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


3

4)

Computer Science/Math (TBN)





Develop computational
algorithms to evaluate therapeutic effects of potential drugs/drug combinations through the
integrated
pancreatic cancer
-
specific pathway/netwo
rk 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

develop
ing, testing
and
validat
ing

a computational platform to s
creen potential drugs and
apply this in
-
silico platform to
assess
the therapeutic efficacy of these drugs/drug panels specific for
pancreatic cancer
.
T
o achieve
this
objective, we have
the following specific aims:



Aim 1
:

Curate
drug
-
protein directionality

specific for pancreatic cancer based on
CMaps/PubMed, and
i
ntegrate
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 interacti
on 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 b
y 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 p
athway for the drug evaluation. W
e
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 net
works

from different data types


drug chemical structures, shared drug targets, drug side effects and drug ontology. We will collect
information for these criteria from popul
ar 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 ident
ify 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 over
-
expressed genes in disease
-
related pathways, drugs should be able to inhibit their expression level to the
normal range. Sim
ilarly, 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 rang
e 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 computation
al connectivity maps, called “C
Maps”
[
1
,
2
]
. Since we focus on pathway level




MURI
Mentor’s Project Proposal

Form, Updated: 11
-
28
-
2012


4

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.



O
ur method incorporates both biological information and traditional graph based model ranking
systems, which provide more biological context while allowing us to uti
lize 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.



O
ur 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 opportu
nity 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 o
n 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 a
nd 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. C
omputational 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 dru
gs 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 prop
osed 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


5

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/protein
-
drug association profiles. The functional approach to drug comparisons helps researchers gain global
perspectives on both the toxicol
ogical 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 construct
ing 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 p
rofile, using
pancreatic cancer
as a case study. We plan to use our current C
-
Map webserver
[
2
]
, global human
annotated and predicted protein interactio
n (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 alg
orithms

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 rever
se 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 dru
gs 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




MURI
Mentor’s Project Proposal

Form, Updated: 11
-
28
-
2012


6

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 inf
ormation. One biology student
, mentored by a graduate student
-

Hui Huang, will
learn and i
mplement 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 P
ET 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 gr
aduate student
-

Hui
Huang), will learn and implement online database
s
,

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 effect
ive 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 com
es 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 Sta
tes. 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 progres
s 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 mo
del 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


7



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 proposa
l?

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

201
3
]

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 2
012


Mar

201
3
]

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 f
indings 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 R
21, 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 (AB
I, 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 w
ill not be released until approval is given by the




MURI
Mentor’s Project Proposal

Form, Updated: 11
-
28
-
2012


8

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]

Lamb, J., et al., The Connectivity Map: using gene
-
expression signatures to connect small
molecules, genes, and disease.
Science

2006,
313
, 1929
-
1935.

[2]

Li, J., X. Zhu, and J.Y. Chen, Building disease
-
specific drug
-
protein connectivity maps from
molecula
r interaction networks and PubMed abstracts.
PLoS computational biology

2009,
5
,
e1000450.

[3]

Mestres, J., Computational chemogenomics approaches to systematic knowledge
-
based drug
discovery.
Current opinion in drug discovery & development

2004,
7
, 304
-
13
.

[4]

Roses, A.D., Pharmacogenetics and the practice of medicine.
Nature

2000,
405
, 857
-
65.

[5]

Yildirim, M.A., et al., Drug
-
target network.
Nature biotechnology

2007,
25
, 1119
-
26.

[6]

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.

[7]

Hopkins, A.L., Network pharmacology: the next paradigm in drug discovery.
Nature
chemical
biology

2008,
4
, 682
-
690.

[8]

Barabási, A.L., N. Gulbahce, and J. Loscalzo, Network medicine: a network
-
based approach to
human disease.
Nature Reviews Genetics

2011,
12
, 56
-
68.

[9]

Lamb, J., et al., The Connectivity Map: using gene
-
expression sig
natures to connect small
molecules, genes, and disease.
Science

2006,
313
, 1929.

[10]

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
.

[11]

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


9


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


10

Principal
Mentor (PM)

Chen, Jake Yue

POSITION TITLE

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

B.S.

1995

Biochemistry and Molecular
Biology

University of Minnesota, Minneapolis

M.S.

1997

Computer Science and
Engineering

University of Minnesota, Minneapolis

Ph.D.

2001

Computer Science and
Engineering

Positions and

Employment History

2010
-

Associate Professor of Informatics, Indiana University School of Informatics,
Indianapolis, IN

2010
-

Associate Professor of Computer Science (joint appointment), Department of
Computer and Information Science, Purdue University
School of Science,
Indianapolis, IN

2007
-

Funding 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

As
sistant 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, S
ina 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, Sup
pl. 3, S7.

4.

Fengjun Li, Xukai Zou, Peng Liu, and
Jake Y. Chen

(2011) New Threats to Health Data
Privacy. BMC Bioinformati
cs, Vol. 12, Suppl
.

12, S7.

5.

Fan Zhang
and
Jake Y. Chen

(2011) HOMER: a human organ
-
specific molecular electronic
repository,
BMC Bioinfo
rmatics
, 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


11

7.

Fan Zhang and
Jake Y. Chen

(2010) A Systems Biology Approach to Discovering and
Validating Breast Cancer Protein Biomarkers in Huma
n 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 O
verview.
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, An
drey 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. Ch
en
, 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 (BIO
KDD 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
Researc
h and Applications, Vol. 3, No. 3.






MURI
Mentor’s Project Proposal

Form, Updated: 11
-
28
-
2012


12


Co
-
Mentor (Co
-
M)

Wu, Xiaogang

POSITION TITLE

Visting Research Scientist, Indiana University
school of Informatics,
Indianapolis

eRA COMMONS USER NAME

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

2009
-

Visiting 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

Professiona
l Experience

2009
-

Associated 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

Jou
rnal 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

201
2,

13
(
Supp 2
):
S1.





MURI
Mentor’s Project Proposal

Form, Updated: 11
-
28
-
2012


13

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 (201
1
) 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, Har
ini N.
Kasamsetty, and Jake Y. Chen
:

HPD: An Online Integrated Human Pathway Database
Enabling Systems Biology Studies.
BMC Bioinformatic
.
2009,
10
(
Supp 11
):
S5.

(*Equally
-
contributed author)

8.

Xiaogang Wu
, and Zuxi Wang
:

Estimating parameters of chaotic systems under noise
-
induced 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 & Fracta
ls

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 im
proving 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, 22:359
-
366.

13.

Lin
g 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
pseudoran
dom 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


14

Section VI: Support Letters
(insert 1
-

2 pages as needed)


Section VII: Appendix
(T
itle 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. Pl
ease i
nsert 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:

09/01/201
1

Date

Completed:

05/01/201
2

Description:

W
e 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) R
etrieve AD
-
associated proteins through the CMaps
platform by using “Alzheimer’s disease” as a query term.
2) R
etrieve AD
-
related pathways by using those
AD
-
associated proteins as input and searching in the Human Pathway Databas
e (HPD) and the PubMed.
3)

I
ntegrate 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)
T
ransform the integrated pa
thways 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
/201
2 (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


15


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
-
pr
otein 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:

04/0
8/2011

(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


Da
te:

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