1. science
2. engineering

STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA
PROGRAM PROPOSAL COVER SHEET
1.
Institution
George Mason University
2. Program action (Check one):
New program proposal ___x__
Spin-off proposal
_____
Certificate proposal
_____
3. Title of proposed program
Bioengineering
5.
Degree designation
Doctor of Philosophy (PhD)
4. CIP code
14.0501
6. Term and year of initiation
Fall 2014
7a. For a proposed spin-off, title and degree designation of existing degree program
7b. CIP code (existing program)
8. Term and year of first graduates
Spring 2018
9. Date approved by Board of Visitors
10. For community colleges:
date approved by local board
date approved by State Board for Community Colleges
11. If collaborative or joint program, identify collaborating institution(s) and attach letter(s) of
intent/support from corresponding chief academic officers(s)
12. Location of program within institution (complete for every level, as appropriate).
Departments(s) or division of ____Department of Bioengineering ____________________
School(s) or college(s) of ___Volgenau School of Engineering_______________________
Campus(es) or off-campus
site(s)_________________Fairfax______________________________
Distance Delivery (web-based, satellite, etc.) _____________________________________
13. Name, title, telephone number, and e-mail address of person(s) other than the institution’s
chief academic officer who may be contacted by or may be expected to contact Council staff
regarding this program proposal.
Joseph Pancrazio, Professor and Chair, Department of Bioengineering
703 993-1605, jpancraz@gmu.edu
TABLE OF CONTENTS
PROGRAM PROPOSAL COVER SHEET .............................................................................................................1
DESCRIPTION OF THE PROPOSED PROGRAM ...............................................................................................1
OVERVIEW .................................................................................................................................................................1
CURRICULUM ............................................................................................................................................................2
COMPLIANCE WITH SACS STANDARD 3.6.2 ..............................................................................................................9
ADMISSION REQUIREMENTS .................................................................................................................................... 10
FACULTY ................................................................................................................................................................. 11
ASSESSMENT ........................................................................................................................................................... 12
BENCHMARKS OF SUCCESS ...................................................................................................................................... 14
EXPANSION OF AN EXISTING PROGRAM ................................................................................................................... 14
SPIN-OFF PROPOSAL ................................................................................................................................................ 15
COLLABORATIVE OR STANDALONE PROGRAM ........................................................................................................ 15
JUSTIFICATION FOR THE PROPOSED PROGRAM ....................................................................................... 15
RESPONSE TO CURRENT NEEDS ................................................................................................................................ 15
EMPLOYMENT DEMAND .......................................................................................................................................... 21
STUDENT DEMAND .................................................................................................................................................. 22
DUPLICATION .......................................................................................................................................................... 22
PROJECTED RESOURCE NEEDS................................................................................................................................. 25
REFERENCES ............................................................................................................................................................ 27
APPENDIX A – RESOURCES ...................................................................................................................................... 31
APPENDIX B – CATALOG DESCRIPTION OF COURSES ............................................................................................... 35
APPENDIX C – SAMPLE SCHEDULES FOR PHD IN BIOENGINEERING ......................................................................... 39
APPENDIX D- ABBREVIATED CV’S FOR THE PHD FACULTY .................................................................................... 40
APPENDIX E – DEPARTMENTAL FACULTY RESEARCH ............................................................................................. 43
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DESCRIPTION OF THE PROPOSED PROGRAM
Overview
George Mason University requests approval to initiate a Doctor of Philosophy (PhD) degree in
Bioengineering. The proposed program will be administered by the Volgenau School of
Engineering with participation from other academic units with related interests at the university.
The program is to be started in the Fall of 2014.
The proposed program is designed to prepare future leaders in bioengineering. The traditional
definition of bioengineering, here used synonymously with biomedical engineering, is to use
engineering techniques to solve problems in biology and medicine. Initially, the field
concentrated in areas closely linked to traditional areas of engineering, such as designing
instruments for the diagnosis of disease, building implanted devices to restore function, and
developing equipment to aid individuals with disabilities. More recently, rapid advances in
understanding the molecular bases of disease has led to bioengineering embracing new
methodologies that combine modern biology, physics, computer science, and engineering.
Bioengineers thus need to be prepared to integrate advanced tools to advance human health (1,2,3).
Advancing human health, however, has resulted in societal challenges. The most widely
discussed one is the cost of health care that has been increasing at an exceptionally rapid rate.
Hardly a day goes by without being reminded that escalating health care costs are a burden to
individuals, their employers, and the nation. Due to the power of technology, bioengineers thus
must become part of making health care not only better, but to make it more efficient and
affordable.
The proposed program is a major step to enabling the bioengineering profession to respond to
societal demands for better and more efficient health care. It is well aligned with the George
Mason University’s mission statement:

"Educate the new generation of leaders of the 21st century men and women capable of
shaping a global community with vision, justice, and clarity."

"Provide innovative and interdisciplinary undergraduate, graduate, and professional
courses of study that enable students to exercise analytical and imaginative thinking and
make well-founded ethical decisions."
This proposed doctoral program is a follow-up to the initiation of an undergraduate program in
2010. The BS in Bioengineering program, with a current enrollment of approximately 120
undergraduates, is administered by the Volgenau School of Engineering through its new
Department of Bioengineering that was established in 2011. The department has 11 full time
faculty members, with 8 holding primary appointments in Bioengineering.
Although the Volgenau School of Engineering is well positioned and able to plan and administer
a graduate program in bioengineering, it decided to plan the proposed doctoral program in
collaboration with other departments and schools at the University. This decision was made in
1
recognition of the need to integrate multiple disciplines within the curriculum and to avoid
duplication and reduce overall cost of program implementation. The nature of the proposed
program is in agreement with a recent report of the National Academy of Engineering: The
Engineer of 2020 (4). The report forcefully advocates engineering education that is broad,
interdisciplinary, and responding to societal needs.
The proposed program targets the development of doctoral-level bioengineers since they are the
ones who will be future leaders of the field. They are the ones who will lead research teams that
generate basic knowledge for the development of appropriate technology, who will be in charge
of many interdisciplinary teams in industry, who will provide intellectual leadership for setting
policy for technology usage and regulation, and who will educate the rapidly increasing number
of bioengineering undergraduates. The need for well-educated bioengineers is underlined by the
Bureau of Labor Statistics that in 2012 projected a “much faster than average” increase in
bioengineering employment between 2010 and 2020 (5).
The program is designed to attract talented students with a BS degree who have already
demonstrated high achievement in a relevant area of science and/or engineering. They also will
be expected to have demonstrated interest in combining engineering and the natural sciences
with discovery and application in the life sciences.
An unique feature of the proposed program is that it expects to attract similarly motivated
students with a wide variety of skills. The program is designed not only to deepen expertise and
skill in a focus area of the student’s choice, but to also give them an opportunity to broaden their
knowledge through meaningful interactions with their peers. They learn not only from faculty
but from each other.
George Mason University has demonstrated major commitment to Bioengineering, and the
proposed program would further help it to further fulfill its commitment. A doctoral program
would make it even more attractive to recruiting and retaining gifted and dedicated researchoriented faculty members, and it would also draw highly qualified students to its programs. It
would provide an example of a forward-looking and unique program that is likely to benefit not
only Mason but that would bring recognition to Virginia. It is also expected to bring public
health improvements and further economic development to the Commonwealth and the nation.
Curriculum
Process
The curriculum was designed after formulating the overall objective and the desired outcomes of
the doctoral program. (These are described in the Assessment section under JUSTIFICATION
OF THE PROPOSED PROGRAM.) It satisfies the university’s broad requirements for doctoral
programs (6), as well as the Volgenau School’s guidelines that allow considerable flexibility for
setting requirements by the individual programs (7). Consistent with the breadth of
multidisciplinary science underlying bioengineering, the program was designed with input from
faculty members from other academic units including but not limited to the College of Science.
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The close interaction will continue to be reflected in the administration of the program. The
Department of Bioengineering within the Volgenau School of Engineering is ultimately
responsible for the proposed doctoral program. The Department will rely substantively on its
Graduate Committee to provide recommendations regarding admissions, curricula, courses, and
examinations. The Graduate Committee will consist of 3 faculty members from Bioengineering,
and at least one from each of the cooperating Schools/Colleges. The program benefits from the
contributions of colleagues from outside the Volgenau School, while the cooperating colleges
benefit from access to a talented pool of graduate students and to new courses that are of interest
to their own students. Faculty members in the cooperating academic units can serve as primary
research advisors to Bioengineering doctoral students; they are also encouraged to serve as
members of doctoral research committees. Each committee is expected to have at least two
faculty members with primary appointment in Bioengineering.
Following are the primary university-mandated requirements for doctoral degrees (6):

Candidates must earn a minimum of 72 graduate credits, which may be reduced by a
maximum of 30 credits from a completed master’s degree or other suitable, approved
transfer work.

Only graduate courses may apply toward the degree.

More than half of all credits (minimum 72) must be taken in doctoral status, after
admission to the degree program.

Candidates must pass a written or oral doctoral qualifying exam and a doctoral candidacy
exam.

Candidates must complete a minimum of 12 credits of doctoral proposal (BENG 998)
and at least 3 credits of doctoral research (BENG 999). A maximum of 24 credits of 998
and 999 may be applied to the degree.

Candidates must pass a final public defense of the doctoral dissertation

Candidates must have a minimum GPA of 3.0 in course work presented on the degree
application, which may include no more than 6 credits of C.
Core requirements
The proposed doctoral program consists of a minimum of 72 credit hours, distributed among the
following categories of courses: Core Science (9 credits), Core Bioengineering (6 credits),
Concentration Courses (18 credits), Dissertation Research (24 credits), and Electives (12 credits).
Concentrations will include: neuroengineering, biomedical imaging, data-driven biomechanical
modeling, and nanoscale bioengineering. All courses that were specifically created for this new
degree program are in bold, and descriptions for the core courses and required concentration
courses are provided in Appendix B.
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Core Science (9 credits):
 Biology Core (3 credits; select one from pool):
o BIOL 682 - Advanced Eukaryotic Cell Biology (3 credits)
o BMED 601 - Cell and Molecular Physiology (3 credits)
o RHBS 710 - Applied Physiology I (3 credits)

Computation/Mathematics Core (6 credits; select two courses of the following):
o MATH 685 - Numerical Methods (3 credits)
o ECE 528 - Introduction to Random Processes (3 credits)
o ECE 535 - Digital Signal Processing (3 credits)
Core Bioengineering (6 credits):
 BENG 501 - Bioengineering Research Methods (3 credits)

BENG 551 - Translational Bioengineering (3 credits)
Technical Electives (15 credits):
These courses are intended to provide students with pre-requisites necessary to pursue upperlevel courses that support the concentrations described below. These courses can include no
more than 6 credit hours outside the Engineering School and a maximum of only 6 credits can be
at the 500-level.
Additional Training and Education
4

Ethics Training: Collaborative Institutional Training Initiative (CITI) Responsible
Conduct of Research course. CITI training modules provide students with an
understanding of conflicts of interest, research misconduct, peer review, authorship, etc.

Bioengineering Seminar: required attendance and participation in a minimum of 3
departmental seminars per semester.

Translational Bioengineering Mentorship: Following successful completion of BENG
551, each PhD student is required to co-mentor with a Bioengineering faculty member an
undergraduate Bioengineering senior design team. The PhD student will apply business,
team work, and entrepreneurship concepts through biweekly meetings with the design
team over two academic semesters. Competency will be demonstrated through faculty
evaluation of the mentorship performance and/or satisfactory performance on senior
design team student surveys.

Teaching Requirement: Each PhD student is required to participate in the department's
teaching activity and demonstrate competency. The requirement is typically satisfied by
working as a recitation instructor for one semester or presenting several lectures within a
course. Competency is demonstrated through either faculty evaluation of the teaching
performance and/or satisfactory performance on student surveys of teaching performance.

Concentration Courses: Students must choose one of four areas offered by the program,
each of which is described below. These four concentration areas were chosen as they
constitute important growth areas for the bioengineering field, reflect the expertise of the
Mason faculty, and provide unique educational opportunities for bioengineering PhD
students within the Commonwealth. Courses offered under each concentration are
designed to provide an in-depth understanding for each area. Students must complete 18
credit hours within the concentration consisting of both required and elective courses.
The elective courses within each concentration must be chosen under the guidance and
approval of the students’ advisors.
Concentrations
Doctoral students choose an approved concentration that reflects important areas of
bioengineering research. Since the field of bioengineering is very broad, we have implemented
concentration areas that leverage faculty expertise to assure that students receive a first-rate
educational experience both in course-work and research. The concentrations consist of a set of
three required classes and three upper-level elective courses within the field of study that are
selected to complement the research and educational experience of the student. All classes listed
are 3 credit hours unless otherwise noted. Currently approved concentration areas and their
requirements are:
Neuroengineering
Neuroengineering involves the use of engineering and computational techniques to understand
neurological function, develop technologies to interface with the neural systems, and design
engineering solutions to restore neurological function lost due disease or injury. This field of
bioengineering draws on the fields of computational neuroscience, experimental neuroscience,
electrical engineering and signal processing. The overall goal is to prepare students to become
independent investigators who are capable of answering important and emergent questions in
neuroscience through the use of engineering and computation.
Required:
BENG 525 – Neural Engineering
NEUR 602 - Cellular Neuroscience
BENG 725 - Computational Motor Control
Three more upper-level courses are to be chosen under the guidance and approval of the
student’s advisor. At least two of the three classes must be at the 700-800 level:
BENG 636 - Advanced Biomedical Signal Processing
BENG 820 – Seminar in Neuroengineering
BINF 740 - Introduction to Biophysics
CS 688 - Pattern Recognition
ECE 738 - Advanced Digital Signal Processing
NEUR 634 - Computational Modeling of Neurons and Networks
NEUR 701 - Neurophysiological Laboratory
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NEUR 734 - Computational Neurobiology
NEUR 735 - Computational Neuroscience Systems
NEUR 751 - Applied Dynamics in Neuroscience
NEUR 752 - Modern Instrumentation in Neuroscience
PSYC 701 – Cognitive Bases of Behavior
PSYC 768 - Neuroimaging
Biomedical Imaging
Biomedical imaging involves the use of engineering techniques in order to develop medical
imaging systems, including both the acquisition of medical images and their processing in order
to support assisted diagnosis and monitoring techniques. This field of bioengineering draws on
the fields of medical physics, signal and image processing, statistics, pattern recognition and
machine intelligence. This curriculum aims to develop a knowledge base for medical imaging
with an emphasis on imaging physics, image processing and machine learning techniques. The
overall goal is to prepare the students to become independent investigators who are capable of
answering important and emergent questions in the field of medical imaging, in terms of
technology development, application, and data analysis.
Required:
BENG 538 - Medical Imaging Physics
ECE 537 - Introduction to Digital Image Processing (DIP)
BENG 738 - Advanced Medical Image Processing
Three more upper-level courses are to be chosen under the guidance and approval of the
student’s advisor. At least two of the three classes must be at the 700-800 level:
BENG 636 - Advanced Biomedical Signal Processing
BENG 830 – Seminar in Biomedical Imaging
CS 659 - Theory and Applications of Data Mining
CS 688 - Pattern Recognition
CS 757 - Mining Massive Datasets
ECE 738 - Advanced Digital Signal Processing
ECE 754 - Optimum Array Processing
OR 842 - Models of Probabilistic Reasoning
PHYS 612 - Physics of Modern Imaging
PSY 757 - Introduction to Bayesian Statistics
PSY 768 - Neuroimaging
STAT 760 - Advanced Biostatistical Methods
SYST 842 - Models of Probabilistic Reasoning
Data-Driven Biomechanical Modeling
Biomechanics involves the use of engineering concepts to (1) measure and describe functions of
biological structures, (2) develop computational models to simulate forces acting on and within
living systems, and (3) design engineering solutions to improve human movement compromised
by disease or injury. Data Driven Biomechanical Modeling leverages multivariate data
acquisition to create computational models that predict biomechanical dynamics. This area in
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Bioengineering draws on the fields of mechanical engineering, imaging, physics, rehabilitation,
physiology, and medicine. The overall goal is to prepare students to not only be able to generate
computational models of human dynamics, but also parameterize these models with the
acquisition of experimental data. This approach enables personalized medicine through the
creation of computational models tailored to individuals with disease or disability. Data sources
include non-invasive bioimaging modalities such as ultrasound and MRI, electromyography, 3D
motion capture and force plates.
Required:
BENG 538 Medical Imaging Physics
BENG 650 - Advanced Biomechanics
BENG 750 - Modeling and Simulation of Human Movement
Three more upper-level courses are to be chosen under the guidance and approval of the
student’s advisor. At least two of the three classes must be at the 700-800 level:
BENG 636 - Advanced Biomedical Signal Processing
BENG 738- Advanced Medical Image Processing
BENG 725 - Computational Motor Control
BENG 850 - Seminar in Biomechanics
CS 795 - Measurement of Human Movement
CSI 742 - The Mathematics of the Finite Element Method
RHBS 711 - Applied Physiology II
RHBS 746 - Neuromusculoskeletal Disability
STAT 662 - Multivariate Statistical Methods
SYST 664 - Bayesian Inference and Decision Theory
Nano-Scale Bioengineering
Most biological processes are transient in nature and originate at the cellular or even sub-cellular
level. Nano-scale bioengineering combines interdisciplinary concepts of engineering at micro- or
nano-scale to address biological problems. It offers the ability to monitor, manipulate and
characterize biological systems under controlled in vitro conditions. Utilizing nanoengineered
materials and devices, non-invasive techniques can be developed to understand and treat diseases
in a completely novel fashion compared to the traditional biomedical approaches.
Based on identification of the current leading research areas, medical and industrial needs, the
curriculum involves a balanced structure of theoretical, experimental and computational
components. Through the following courses, students will learn the concepts of fluid mechanics
which will allow them to design and create microdevices and study biological problems at the
cellular and physiological levels. Ultimately, nano-scale bioengineering will provide a solid
theoretical background and practical experience in diverse areas such as tissue engineering,
biopharmacy and diagnostics.
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Required:
BENG 541 - Biomaterials
BENG 641 - Advanced Nanotechnology in Health
BENG 745 - Biomedical Systems and Microdevices
Three more upper-level courses are to be chosen under the guidance and approval of the
student’s advisor. At least two of the three classes must be at the 700-800 level:
BENG 840 - Seminar in Nano-scale Bioengineering
BINF 740 - Introduction to Biophysics
BIOL 669 - Pathogenic Microbiology
CHEM 641 - Solid State Chemistry
CHEM 660 - Protein Biochemistry
CHEM 728 - Introduction to Solid Surfaces
CHEM 733 - Polymer Physical Chemistry
CHEM 814 - Advanced Bioorganic Chemistry
CHEM 833 - Physical Chemistry and Biochemistry
CSI 780 - Computational Physics and Applications
CSI 720 - Fluid Mechanics
NANO 620 - Computational Modeling in Nanoscience
Dissertation Research
Students are expected to complete 24 credits of BENG 998 and BENG 999 towards their degree.
Students cannot enroll in BENG 999 before they have advanced to candidacy. Students advanced
to candidacy after the add period for a given semester must wait until the following semester to
register for BENG 999. Students cannot advance to candidacy and defend their dissertation
during the same semester. Once enrolled in BENG 999, students must maintain continuous
registration in BENG 999 each semester until graduation, excluding summers. Students who
defend in the summer must be registered for at least 1 credit of BENG 999 during that summer
term.
BENG 998 - Doctoral Dissertation Proposal (Credits: 1-12; student must complete a minimum
of 9 credits)
BENG 999 - Doctoral Dissertation (Credits: 1-12; student must complete a minimum of 3
credits)
Student Progress and Advising
The typical time course of completing the program is shown in the sample schedules in
Appendix C; academic and research advisors are responsible for providing students with
guidance about their progress through the program.
8
After acceptance, students are assigned an academic advisor who is a member of the
Bioengineering Department. As students familiarize themselves with Mason and they develop
specific research interests, they need to choose a research advisor. If the research advisor is a
faculty member in Bioengineering, the same person typically also becomes the student’s
academic advisor. If the research advisor is not a faculty member of the Bioengineering
Department, the student typically retains his or her own academic advisor, benefiting from both
advisors.
Qualifying Exam
All students entering the Bioengineering PhD program will be required to pass a qualifying exam
which consists of two phases: a Technical Qualifying Exam (TQE) and a Research Qualifying
Exam (RQE). The TQE is an in-class written exam that tests knowledge of core bioengineering
concepts as well as competency in mathematics and computation. A score of at least 80% is
required to pass. The RQE consists of a written report and oral presentation and aims to assess
the ability of the student to communicate effectively. Students will be required to define a
research problem and explain the significance, critically review the literature related to the
research problem, describe appropriate research methods to study the problem, and interpret and
communicate their results. The RQE topic will be defined by the faculty advisor in consultation
with the student. The topic may be related to the eventual thesis topic. A committee of at least
three faculty members which includes the advisor will evaluate the written report and the oral
presentation. During the presentation the student will be expected to answer questions about their
project and about fundamental concepts related to the research. The committee will vote to
determine whether or not the student has successfully passed the RQE.
Students entering with an MS degree will take the TQE within their first year in the program and
the RQE prior to completing 12 credits in the PhD program. Students entering with a B.S. degree
will take the TQE after completing 18 credits of coursework and the RQE prior to completing 36
credits in the program.
After a student has taken both the TQE and the RQE, the Bioengineering PhD Committee will
review the exam results, the student's transcript, and a letter of recommendation from the
student's advisor. Based on this information, the PhD Committee will determine whether or not
the student is qualified for the PhD program. If the student does not qualify on their first try, the
student will be allowed to repeat one or both of the exams in the following year. The TQE and
RQE may be repeated once. A student who fails to qualify on their second try will be removed
from the program.
Compliance with SACS Standard 3.6.2
The proposed program complies with SACS standard 3.6.2: The institution structures its
graduate curricula (1) to include knowledge of the literature of the discipline and (2) to ensure
ongoing student engagement in research. In Table 1, relevant courses for these two standards are
identified. While the proposed Bioengineering PhD program draws upon a range of existing
upper-level graduate courses to provide a comprehensive educational experience for students, the
table below emphasizes Bioengineering (BENG) courses. In addition to the coursework, the
9
proposed program requires students to pass a written comprehensive exam prior to advancement
to candidacy. The comprehensive examination requires a thorough knowledge of the literature in
Bioengineering. A Dissertation project is also a requirement of the proposed degree program.
Thus, a substantial independent research project must be completed and defended.
Course
Knowledge
of the
Literature
BENG 501: Bioengineering Research Methods
X
BENG 525: Neural Engineering
X
BENG 538: Medical Imaging
X
BENG 541: Biomaterials
X
BENG 551: Translational Bioengineering
X
BENG 641: Advanced Nanotechnology in Health
X
BENG 636: Advanced Biomedical Signal Processing
X
BENG 725: Computational Motor Control
X
BENG 738: Advanced Medical Image Processing
X
BENG 745: Biomedical Systems and Microdevices
X
BENG 820: Seminar in Neuroengineering
X
BENG 830: Seminar in Biomedical Imaging
X
BENG 840: Seminar in Nano-scale Bioengineering
X
BENG 850: Seminar in Biomechanics
X
BENG 998: Doctoral Dissertation Proposal
X
BENG 999: Dissertation Research
X
Table 1. Bioengineering Courses that Address SACS Comprehensive Standard 3.6.2
Research
X
X
X
X
X
X
X
Admission Requirements
Applicants to the proposed program must fulfill university-specified graduate application
requirements specified in detail by Mason’s catalog (6). Requirements for the Volgenau School of
Engineering are flexible, allowing programs to formulate requirements in addition to those of the
university (7). Accordingly, the proposed program will require submission of the following
documents for considering admission:





Completed Application for Graduate Study
Official transcripts from all prior colleges or universities attended
Goals statement
Three letters of recommendation
Official GRE exam scores
The submitted documents are used to determine whether an applicant meets the program
requirements. These requirements have been selected to assure that admitted students have a
strong scientific or engineering background, have a high potential of earning a doctorate in the
10
proposed bioengineering program, and are likely to be successful as future bioengineering
leaders. The required qualifications are thus as follows:

BS degree in engineering or the sciences relevant to the doctoral program. A GPA of at
least 3.3 is expected, which is higher than the university-mandated 3.0.

Demonstrated interest in combining engineering and the natural sciences with discovery
and application in the life sciences. Examples of demonstration include a degree that
reflects the desired combination (such as bioengineering, biophysics); a degree in
engineering or the natural sciences but also having taken courses in the life sciences; a
degree in biology but also having taken courses in mathematics, physics, or engineering;
having had project or research experience that combined complementary expertise.

Past experience and recommendation letters indicating likely success in studies for a
doctorate in bioengineering.

Past experience, recommendation letters, and/or goals statement indicating genuine
interest in benefiting society through leadership in research or the application of research
to human health.

Students will also be required to submit GRE scores. The minimum GRE scores fpr
admission will be 75th percentile in the quantitative section, with 50th percentile on the
verbal and quantitative sections. International students are required to also submit a
TOEFL score as required by George Mason University. The minimum TOEFL score for
the proposed program will be 80, which is consistent with the new requirements for the
Volgenau School of Engineering.
The submitted materials will be reviewed by the Graduate Committee of the Bioengineering
Department which will then make a recommendation to the department chair. The Committee
will also determine the number of credits the applicant is to be given if he or she applies with an
MS degree or has taken previous courses in a relevant graduate program. The maximum number
of credits that may be transferred is 30.
As mentioned above, the Graduate Committee will consist of three full-time members of the
Department, as well as at least one full-time member from each of participating School of
College. Involvement of these participants is essential for assessing the applicants’ expertise and
their prospects for conducting studies in areas where the primary research strength at Mason is
outside the Volgenau School.
Faculty
The proposed program will be served by a diverse and superbly qualified faculty. As part of its
initiative in bioengineering, The Volgenau School recruited 1 part time and 10 full time faculty
members since 2006. The home-department of each was determined by the “best-fit” and the
candidates’ preferences. The following are the home departments for the current bioengineering
faculty:
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Bioengineering: 8 full time, 1 part time
Computer Science: 2 full time
Electrical and Computer Engineering: 1 full time
In addition, the proposed program has the participation of several other faculty members in
academic units outside of the Department if Bioengineering. These faculty members have
backgrounds in a variety of mathematical, physical and life sciences and will provide additional
educational and research opportunities to bioengineering students. They offer courses, provide
research opportunities, participate in program planning, and help make admission decisions.
They have all ascertained their interest in being affiliated with the proposed program. The
academic units of current affiliated faculty members are the following:







Dean’s Office, Volgenau School of Engineering
Department of Computer Science, Volgenau School of Engineering
Department of Molecular Neuroscience, College of Science & Krasnow Institute
Department of Psychology, College of Science
Div. of Health and Human Performance, College of Education and Human Development
School of Physics, Astronomy, and Computational Sciences, College of Science
School of Systems Biology, College of Science
Appendix D gives a brief description of current faculty members associated with the proposed
doctoral program. This list is expected to be dynamic; it will change as new faculty members are
recruited and as research interests change. Recognizing a new affiliate faculty member requires
the approval of the BE faculty and current affiliate faculty.
A measure of the faculty’s capacity to support doctoral-level research is external funding. The
Bioengineering faculty members from within the Volgenau School of Engineering hold grants
from prestigious federal agencies including the NIH, NSF, and DARPA. A detailed description
of currently funded research programs is provided in Appendix E.
Assessment
The overall objective of the program is to educate future bioengineering leaders who will
contribute substantially to improving health through research, education, business or government
service. Since assessing the achievement of this overall goal would require some eight to ten
years (examining education and career), desired outcomes are needed that can be assessed on a
more realistic time scale. These outcomes are formulated as desired results of the educational
program that can be examined as the program is being implemented. The outcomes thus can be
used to serve as the basis of making continuous improvements to the program.
The program expects to achieve the following outcomes that will contribute to the graduates
becoming leaders in bioengineering:
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
Graduates, relying on a strong understanding of the life sciences, will demonstrate the
ability to formulate and perform research that addresses significant health-related
problems.

Graduates will have a strong background in areas such as mathematics, physics,
engineering, and computer science to allow them to perform, lead, or evaluate research
that uses advanced quantitative techniques to solve health-related problems.

Graduates will have had experience in making, planning and/or interpreting biomedical
measurements.

Graduates will understand their responsibility to publish results and share knowledge, and
they will communicate effectively as speakers, writers, and educators.

Graduates will understand the need for translating research results into devices or
procedures that will benefit society. Thus, they will be familiar with such concepts as
commercialization, entrepreneurship, intellectual property, regulation, safety, value,
effectiveness, efficiency, and cost.
Assessing the achievement of these outcomes is a two-stage process. The first is conducted
during and after selected courses that address these outcomes. Course instructors will assess the
degree of progress toward achieving relevant outcomes on a scale of 5 (excellent) to 1 (poor) for
each student. These achievement scores will be given on the basis of the students’ performance
on tasks specifically related to one of the outcomes, and they are assigned in addition to the
regular letter grades.
The second stage is aggregating the achievement scores assigned in the courses, and evaluating
the overall achievement of the outcomes. This is done annually at the end of the academic year
by a special joint meeting of the faculty and the Bioengineering Graduate Committee. At this
meeting the faculty considers, for each doctoral student, the achievement scores in each course
taken during the year. It also considers comments made by the student’s advisor(s) and any other
faculty member(s). Following discussion, the faculty will determine an overall achievement
score for each outcome, and determine if any action is needed that might improve the student’s
achievement.
Following the evaluation of individual students, the faculty will consider the effectiveness of the
program itself to help students achieve the desired outcomes. This consideration will consider
issues such as topics in individual courses, scheduling of courses, prerequisites, effectiveness of
teaching, performance in research, and methods of determining outcomes. Based upon the
discussion, ways of improving the programs will be adopted in the expectation that the changes
will enhance the achievement of the desired outcomes.
Evaluating the program objective will be made for the first time three years after graduating the
first student. Since achieving this objective is Benchmark #7, its assessment is described in the
next section.
13
Benchmarks of Success
The faculty of the proposed program has adopted the following benchmarks of success:
1. At least 4 students will enroll in each of the first two years of the program. New
enrollment will be at least 6 students each year in subsequent years.
2. 60% of students who enroll earn a doctorate within 5 years. 70% earn a doctorate within
6 years.
3. Each student will have achieved a score of 3 on at least four of the five outcomes during
each annual evaluation.
4. On each outcome, at least 70% of students achieve a score of 3 or better.
5. At least 80% of the students will have found a job or postdoctoral position within 3
months of graduation.
6. At least 70% of the graduates will have a leadership position within five years of
graduation, contributing substantially to improving health through research, education,
business or government service.
The achievement of these benchmarks will be assessed annually at the same faculty meeting that
is used to assess the achievement of outcomes as described in the previous section. Two of these
(#3 and #4) are clearly linked to the outcomes and are readily available from their assessment.
The process of determining whether benchmarks #1, #2, and #5 are met will be based on readilyavailable or obtainable numeric information.
Determining whether benchmark #6 is met is much more difficult. It can be performed for the
first time only 5 years after the graduation of the first student, and it must depend on information
received from the career path of the graduates. The information will include a then current
curriculum vitae, as well as responses to a survey that deals with the graduates’ self-assessment
of their careers and academic preparation. Attention will be given to maintain continuous contact
with the graduates since their help and collaboration is essential for continuous program
improvement.
Judging whether a graduate is in a “leadership” position and whether he or she contributes
“substantially” is subjective. Consequently, the faculty’s evaluation of meeting benchmarks will
be shared with the Bioengineering Advisory Committee that includes representatives from the
program constituents: universities, industry, and government. Final action on correcting any
shortcomings an/or improving the program will be made taking into action the Committee’s
feedback.
Expansion of an Existing Program
14
The proposed program is new and not an extension of an existing one.
Spin-off Proposal
The proposed program is new and not a spin-off from an existing one.
Collaborative or Standalone Program
This is a standalone program developed and operated by George Mason University. No outside
organization was involved in its development, and no other organization is responsible for its
operation.
JUSTIFICATION FOR THE PROPOSED PROGRAM
Response to current needs
This section first gives a brief overview of the relatively new field of bioengineering, often
interchangeably referred to as biomedical engineering, and how bioengineering is changing
along with engineering in general. It then describes the challenges that the nation's health care
system faces and the role that bioengineers can play in meeting them. The section emphasizes the
economic and social reasons why the Commonwealth of Virginia needs to play a role in
educating bioengineers leaders who will advance knowledge, contribute to economic
development, and help solve health care problems. The section concludes by showing why
Mason is uniquely positioned to initiate a doctoral level bioengineering program of major benefit
to the Commonwealth and the nation.
What is bioengineering?
According to the Biomedical Engineering Society, “A Biomedical Engineer uses traditional
engineering expertise to analyze and solve problems in biology and medicine, providing an
overall enhancement of health care" (8). Similar definitions of the field are given by numerous
other sources (9,10,11). Such a definition dates back to the field’s beginning in the early 60’s when
a few institutions initiated degree-granting programs in biomedical engineering or
bioengineering. Typically, using techniques used by electrical or mechanical engineering, the
stated purpose of the programs was to bring “engineering rigor” to biology by adding
quantitative approaches to a field considered primarily descriptive. Thus, much work was done
on recording and analyzing signals derived from the human body, such as the electrocardiogram,
electroencephalogram, electromyogram, that give diagnostic information on the heart, brain, and
muscles, respectively. The descriptions often relied on models that used mathematical
approximations to characterize the living system. The rapid development of new modes of
imaging, including CAT (three-dimensional X-ray), MRI, and PET, has resulted in additional
ways to quantify the structure and function of the body. Devices that improve or replace
15
function, such as pacemakers, cochlear implants, and prosthetic limbs, have benefited individuals
living with disability.
The major role of bioengineering as a contributor to human health is reflected in the field's
current definition by the National Institute of Biomedical Imaging and Bioengineering (12) of the
National Institutes of Health, the nation’s premier health-related research institution with an
annual budget of over $30 billion. According to this definition:
"The discipline of biomedical engineering lies at the forefront of the medical revolution.
Advances in biomedical engineering are accomplished through interdisciplinary activities that
integrate the physical, chemical, mathematical, and computational sciences with engineering
principles in order to study biology, medicine, and behavior."
After giving examples of technological developments similar to those above, the definition
concludes:
"The goal of bioengineering is to promote biomedical advances to diagnose and treat disease and
to prolong a healthy and productive life."
The changing nature of engineering
The societal linkages of technology and health care were mentioned among the reasons for the
recommendations of an influential report by the National Academy of Engineering, The
Engineer of 2020 (4). The Executive Summary of the report states that “[t]he steady integration of
technology in our public infrastructures and lives will call for more involvement by engineers in
the setting of public policy and participation in the civic arena.” (p 4) The report concludes that
engineering must not be a narrow, strictly technology-oriented profession, but that the engineers
of the future must have an exposure to a wide array of disciplines.
The engineering community has endorsed the recommendations. The Harvard Crimson proudly
stated (13) that the university’s “push to expand its Division of Engineering and Applied Science,
begun in 2001, falls directly in line with recommendations released this past June by the National
Academy of Engineering” that “called for engineering departments to widen their focus and to
include more interdisciplinary work, both in research and teaching”. Since both research and
teaching are performed primarily by doctoral-level graduates, the need is especially acute at this
level. Likewise, a recent editorial in the Annals of Biomedical Engineering, the official journal
of the Biomedical Engineering Society, stated that “[a]s biomedical engineers, we are asked to
interact and work well with those in other areas. … Our graduates need to know not just the
engineering aspects of a problem, but also its physiological, biological, business, regulatory, and
legal facets” (3). Again, the complexity of the task requires a doctoral-level educational program
with breadth that can meet the stated need.
The proposed doctoral program in bioengineering is a response to the challenge raised both by
the National Academy of Engineering, as well as by the Annals of Biomedical Engineering. We
aim to train the next generation of bioengineering leaders.
What are the current needs?
16
Although health care is improving due to advances in both biology and technology, there are
major challenges. The following describes four of them.
Biomedical research at multiple interfaces
Basic discoveries in the life sciences are made at an unprecedented pace, increasingly aided by
natural scientists, mathematicians, and engineers. Discovery of the molecular structure of the
DNA, our genetic code, and the way it is translated into proteins, the building blocks of living
organisms, has revolutionized our understanding of the origin of many diseases. It is now known,
for example, that some abnormalities are caused by a specific single alteration in the DNA code,
and some by a complex pattern of changes (14).
Understanding of the underlying cause, however, does not imply easy remedy. A wrong protein
that is generated by a mistake in the genetic code causes cell alterations that also alter the tissues
that are formed by the aggregation of cells. Altered tissue leads to altered organs, possibly
causing debilitating diseases. The complex transformation from DNA to proteins, to tissues, and
then to organs is further complicated by the significant influence of the environment on the
transformation.
Understanding such staggering complexity and devising new therapies requires new skills for
bioengineers. They may be called upon to design instruments for making measurements on cells,
tissues, or organs, and to devise technologies for studying or controlling molecular to organ-level
processes. As opposed to the traditional methods of studying and attacking problems at a single
level, multi-level approaches are needed to derive optimum benefit (15,16,17). For example, it is no
longer sufficient to study molecular-level alterations of protein structures: eventual clinical
applications require that the effects of molecular alteration be followed to tissue and organ
system levels where clinical abnormalities are often first identified.
Bioengineers are thus needed to conduct basic research at many stages, often in collaboration
with biologists and physicians, to determine the best technological approaches for experiments,
find patterns in the data, and to develop and then test models. For example, silicon-based gene
chips are used to identify molecular abnormalities that are associated with diseases (18), imaging
and computational technologies now allow visualization of structures at molecular, cellular, and
tissue levels that suggest new diagnostic and therapeutic methods (19,20), and computer-aided
searches of huge amounts of data can serve as the basis for discoveries (21). The critical role of
bioengineers was emphasized in a 2009 Science editorial, The Next Innovation Revolution, by
Susan Hockfield, President of the Massachusetts Institute of Technology (22). She wrote:
“These revolutions [convergence between engineering and the physical sciences,
discovery of the structure of DNA] sowed the seeds of a third revolution that links the life
sciences with engineering and the physical sciences in powerful new ways.”
The proposed program will generate bioengineers who will respond to this revolution in a
thoughtful, responsible, and expert manner, becoming leaders and educators of their profession.
17
Appropriate use of technology in medicine.
Because of increasing knowledge, patients face a great variety of devices and procedures when
they are being diagnosed or treated. Their organs are often monitored electrically, chemically,
mechanically, and visually when being diagnosed. Therapy likewise includes technological
interventions. Patients who are disabled by disease, accident, or war are often helped by
sophisticated wheelchairs, as well as by implants designed to restore function (23,24). Pacemakers
have been used for decades to initiate heartbeats, and cochlear implants are increasingly used to
improve hearing (25). Deep brain stimulation has led to dramatic improvements in some patients
with Parkinson's disease (26), and research is advancing to restore vision through retinal or brain
implants (27), and using closed-loop control of epilepsy by transcranial electrical stimulation (28).
A new approach to treating chronically elevated blood pressure that does not respond to
medications is the RF ablation of the sympathetic nerve to the kidney (29).
In addition to these primarily technology-based developments, biological discoveries have
introduced a whole new area of diagnostic and potential therapeutic procedures that are
molecular and cell-based. Genetic testing is used to identify the type of cancer that may respond
to a particular class of drugs (30), and there are efforts to treat Type I diabetes by implanting
properly encapsulated insulin-producing beta-cells into the diseased pancreas (31). Efforts are also
underway to use stem cells implanted into the damaged myocardium in the expectation that they
may be coaxed into becoming healthy muscle cells (32).
While many of these devices and potential approaches are helpful or even life-saving, others may
be of questionable utility. Is it beneficial, for example, to have an instrument that increases the
accuracy of a measurement by 2%? Of course, it depends on many factors, including whether the
improved accuracy would materially influence the choice among treatment options. If so, what is
the benefit and cost to the patient of each possible treatment? If the treatment is costly, are there
alternative measurements that may provide diagnostic information that do not require such a high
precision?
An ability to answer such questions is essential while debates about the future of the US health
care system are prevalent, and when the rapid increase in health care costs, now approximately
18% of GDP is a major societal concern (33,34). Although some say that it is the cost of
technology that is at least partially responsible, others argue that it is the inappropriate use of
technology such as multiple and unnecessary tests, and "trial and error" approaches of therapy.
Regardless, it is now imperative that improvements in health care be accompanied by
recognizing their costs. It is thus no longer acceptable to develop a device or procedure that
performs "better", it must perform better in a “cost-effective” way.
Any analysis of cost-effectiveness must consider the question: “Cost for whom?” The answer
may be different for the patient, hospital, insurance company, state, or society. While the
bioengineer may not be an expert in all relevant areas, he or she must be able to ask the right
questions, and evaluate the answers in a critical manner. Bioengineers of the future, especially
those in leadership positions, must use their knowledge of technology, biology, and cost
consciousness to bring about value, taking into account medical benefit as well as cost to patient
and society (35).
18
One of the expectations is that "personalized medicine" will go a long way toward delivering
more cost-effective health care (36). This approach uses genetic analysis to diagnose the patient's
disease accurately and with great specificity, and then suggest the most appropriate treatment
based on that analysis. The approach relies heavily on technology, involving gene or protein
microarrays for data acquisition, analyzing the data by powerful computational techniques, and
developing drugs, processes or devices that target the disease on a scientifically sound basis. In
his book, "The Language of Life: DNA and the Revolution in Personalized Medicine”, Francis
Collins, Director of the National Institutes of Health, gives an excellent description of how
patient health can be improved by practicing such “personalized medicine” (14). Such therapy
would avoid the usual trial and error process, speed recovery, and reduce costs by avoiding
ineffective therapies.
Informed decisions about appropriate technology to serve a medical need must integrate
technological, biological, and societal information relevant to the question; they cannot be made
on the basis of just one consideration alone. In many cases it is the modern bioengineer, having
had an education that stressed the need for cross-disciplinary integration, may be the best
equipped to formulate an answer.
Enhancement of the economy
The US biomedical industry has been one of the leaders in the world, and it is still highly
competitive. For example, the US medical device market, estimated to be $106 billion, is the
world’s largest (37). Seven of the world’s top ten medical device manufacturers are US
companies. The industry directly employs some $400,000 Americans, and it doubled its exports
to $33 billion between 1998 and 2008 (38).
International competition, however, is keen, providing growing challenges. For example, imports
constitute an increasing (now 32%) of the US medical market, and outsourcing of manufacturing
and the emergence of major new industries in China and India are causes for economic concern
(37,38)
. Strict regulatory standards help ensure safety, but they also provide motivation for research
and development, as well as clinical trials, to be conducted overseas.
In spite of the challenges, due to the aging population and needs arising in emerging markets, the
overall outlook is promising, predicting that the “medical device industry will be fueled by
scientific progress in this new century of the life sciences, as fundamental discoveries and
advances in computing, materials, engineering, and physics create the knowledge base for an
explosive growth in the creation of new treatments and cures” (38). Integrative science,
innovation, and efficiency are the keys to competitiveness, and appropriately educated
bioengineers are well equipped to bring these to the biomedical industry. As discussed above,
using their technical expertise, knowledge of the life sciences, as well as their recognition of the
need for cost control, they are well positioned to develop innovative and cost-efficient products
that will help the US biomedical industry maintain its leadership position.
The Commonwealth of Virginia is well positioned to participate in the economic benefits
brought about by bioengineering. As described in the Washington Business Journal, Virginia
19
plays a central role in the striving DC-area biotech industry (39). Greater Washington has the
fourth largest concentration of biotechnology employment, and Virginia directly employs over
20,000 people in biotechnology organizations, with the number growing to 80,000 when
counting those who are involved indirectly. Virginia’s biotech sector is said to have generated
$13 billion worth of products and services in 2008. Bioscience employment in the state grew by
23% from 2008 to 2011, compared to a 6% overall growth (40).
The Commonwealth has recognized that a striving economy requires excellence in research,
innovation, and technology. For example, The Virginia Biotechnology Research Park in
Richmond is expanding, currently having over 1.1 million square feet of research and office
space in Richmond (41). The Virginia Initiative for Science Teaching and Achievement (VISTA)
partnership secured a five-year $28-million grant from the US government in 2010 to improve
science teaching and learning at all educational levels throughout the state (42). George Mason
University plays a leadership role in this partnership. Also, in May 2012, three new STEM
Academies were approved by the Virginia Board of Education, bringing the total number to 14
(43)
. One of these academies is in Fairfax county, home to Mason.
The summary of Virginia’s 2012-14 budget emphasizes the central role higher education plays in
the state’s vision (44). The document unequivocally states that “There is no more important
investment Virginians can make than in the future of those students who are currently studying
to become the next leaders of this great Commonwealth”. The budget includes $100 million per
year to enhance studies in science, technology, engineering, math and healthcare (STEM-H).
Although the doctoral program was conceived before the publication of this budget, the program
is clearly consistent with the state’s commitment to STEM-H education.
Educating bioengineers of the future
Although the needs above apply to all bioengineers, they are especially acute for those with a
doctorate. In general, doctoral-level bioengineers are expected to achieve higher levels of
professional responsibility than those with a BS or MS. Consequently, they are expected to
become leaders, providing advice, guidance, and example to their more junior colleagues.
Historically, a doctorate is considered to be helpful for leadership in industry and government,
while essential in academia. Research universities depend on their research accomplishments,
requiring highly trained faculty members who can succeed in a competitive environment. Thus,
the education of the next generation of bioengineers is in the hands of those holding a doctorate,
necessitating forward-looking doctoral programs in bioengineering nationally and across the
Commonwealth.
It has become increasingly recognized that significant opportunities exist for PhD Bioengineers
in industry either as employees of large firms or as entrepreneurs seeking to turn research into
marketable biomedical products (45). In spite of the extensive scientific/engineering training that
PhD graduates typically attain, many businesses view graduates hesitantly because they lack core
skills and knowledge such as leadership and teamwork, intellectual property, regulatory affairs,
and business plans. To capitalize on opportunities in program management and emerging
20
leadership opportunities in the Washington DC metropolitan area, the proposed program requires
training in translational bioengineering.
Why Mason?
Although the proposed program is likely to benefit the entire nation, the primary beneficiary is
expected to be Virginia. Mason is located in Northern Virginia that has an entrepreneurial spirit
(46)
, rapidly growing and highly educated workforce, drawing highly trained engineers, scientists
and physicians to state-of-the-art facilities. It has the state’s largest concentration of
biotechnology companies, accounting for 34% within Virginia (40). According to figures based on
2010 and 2011 US census data, 27% of Fairfax County’s population of age 25 or over has a
graduate or professional degree, and 29% at least a Bachelor's degree (47). The corresponding
figures are 10% and 18%, respectively, for the US population. Such a well educated population
in the university's immediate vicinity is both a major resource and potential beneficiary of the
proposed program, as well as a source of students for Mason’s current and proposed
bioengineering programs. There are no other doctoral programs in bioengineering in
Northern Virginia, so Mason’s program is expected to fill a major void.
Northern Virginia provides excellent opportunities for educating bioengineers who are aware of
society's needs. The proximity of national laboratories (such as the National Institute of
Standards and Technology, and Naval Research Laboratory), regulatory agencies (such as the
Food and Drug Administration), and funding agencies (such as the National Institutes of Health,
National Science Foundation, and the research arm of the Department of Defense) are major
resources. The area also has abundant non-government research and clinical facilities, such as
the Janelia Farm research laboratories of the Howard Hughes Medical Research Institute,
INOVA Clinical Center, and the Children’s National Medical Center. Mason has had extensive
interactions with these institutions, some of which have already provided joint research, training,
and employment opportunities.
Employment Demand
Bioengineering is a rapidly growing profession. According to a March 29, 2012 report of the
Bureau of Labor Statistics (5):
“Employment of biomedical engineers is expected to grow by 62 percent from 2010 to
2020, much faster than the average for all occupations. Demand will be strong because an
aging population is likely to need more medical care and because of increased public
awareness of biomedical engineering advances and their benefits.”
The same report states that “[b]iomedical engineers work in manufacturing, universities,
hospitals, research facilities of companies and educational and medical institutions, teaching, and
government regulatory agencies.” Many of these professional affiliations require a doctorate
degree.
21
The rate of expected increase is especially striking when compared with those predicted for any
of the other listed engineering areas (48). The next two largest increases are 22% (for
Environmental Engineering) and 19% (for Chemical Engineering).
Occupational projections are also available for Virginia, compiled by the Virginia Workforce
Connection (49). Growth of estimated employment from 2008 to 2018 is estimated to be 88% for
biomedical engineers, compared with that of 19% for all engineers.
To ascertain the need for doctoral level biomedical engineers, a search has been conducted
through several employment sites.
Student Demand
Two sources of student demand for the Ph.D. in Bioengineering are provided: 1) a survey given
to undergraduate students; and 2) letters from prospective students who would want to enroll in
the proposed program.
STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA
SUMMARY OF PROJECTED ENROLLMENTS IN PROPOSED PROGRAM
Projected enrollment:
Year 1
Year 2
Year 3
Year 4
Target Year
(2-year institutions)
Year 5
Target Year
(4-year institutions)
2014 - 2015
2015 - 2016
2016 - 2017
2017- 2018
2018 - 2019
HDCT FTES HDCT FTES HDCT FTES HDCT FTES
6
4
11
7
16
9
21
12
GRAD
HDCT
FTES
GRAD
--
21
12
5
Duplication
First and foremost, there are no other bioengineering doctoral programs available to students in
Northern Virginia. Three universities in Virginia offer doctoral degrees in biomedical/medical
engineering: University of Virginia (UVA), Virginia Commonwealth University (VCU), and
Virginia Tech (VT). None of these programs offer any bioengineering degree programs in
satellite campuses in Northern Virginia. The number of such degrees awarded by each institution
in the past five, based on data published by SCHEV’s C1.2 report for CIP code 14.051 (50), is
22
listed in the Table below. The total number of annual graduates ranges from 20 in 2008-09 to 26
in 2010-11.
UVA
VCU
VT
2007-8
15
3
5
2008-9
11
3
6
2009-10
15
4
3
2010-11
17
1
8
2011-12
8
7
6
Based on descriptions at each of the program’s website (51,52,53), emphasis areas for the programs
are:
UVA
Cardiovascular bioengineering
Biomedical and molecular engineering
Cellular and molecular bioengineering
Computational systems bioengineering
Tissue engineering and biomaterials
Musculoskeletal bioengineering
VCU
Biomedical imaging systems
Orthopaedic biomechanics
Tissue and cellular engineering
Biomaterials
Artificial organs
Human-computer interfaces
Cardiovascular devices
Rehabilitation engineering
VT with Wake Forest University (WFU)
Tissue Engineering
Biomedical Imaging
Nanomedicine & Nanobioengineering
Biomechanics
Neuroengineering
Translational Cancer Research
Cardiovascular Engineering
There are both similarities and differences between Mason’s initially proposed four areas and
those offered by other Virginia doctoral programs. The similarities arise by the recognized
importance of targeted research areas, and the emergence of new technologies that are likely to
provide major advances in these areas. An example is the broad area of imaging. The differences
are due to the variety of faculty and environment, enabling the use of approaches that are best
suited to available resources at each of the universities. For example, the emphasis on datadriven biomechanical modeling, where students develop skills in both imaging and
23
biomechanics, is an emphasis at Mason but not at the other universities. Likewise, leveraging the
strength of Mason’s Krasnow Institute in Neuroscience with key Volgenau School faculty has
led to our prominent concentration in neuroengineering, a focus area somewhat shared with
VT/WFU. Conversely, while tissue engineering is a major focus area at other universities, it is
not at Mason.
A noteworthy distinct feature of the Mason program is the requirement that students acquire
understanding and appreciation of translational bioengineering. The goal with this requirement is
to prepare students to be effective leaders and program managers spanning academic research
laboratories, federal laboratories, and industry.
Another difference between Mason’s and the approaches by the other three universities arises
from differing academic/administrative arrangements. All three existing doctoral programs rely
on close interaction between engineering and medical schools: UVA and VCU have their own
medical schools, while the program at VT is a collaborative initiative between VT’s engineering
and Wake Forest University’s medical school. Since Mason does not have a medical school, the
proposed program brings about biological and medical collaborations in a different way by
leveraging collaborative opportunities at Mason. The Mason collaborations depend on and
benefit from the wide range of institutions both within and in the vicinity of the university. As
described earlier, the proposed program aims to have the participation of multiple academic units
of the university, bringing about a merging of expertise that is unusual even among
bioengineering programs.
It is well known that Washington, DC metropolitan area provides extraordinary educational
opportunities. While research laboratories are available at most universities, the Northern
Virginia and the DC area abounds in national-level government and private laboratories. The
availability of policy-making and regulatory agencies is an exceptional asset for educating
bioengineering leaders, an asset that the proposed program will actively utilize. Already existing
collaborations with existing hospitals ensure clinical contact that introduces “reality” often
missing from purely scientific and theoretical studies.
Rather than being duplicative, the proposed program complements and enhances capacity for
educating bioengineers in the Commonwealth to meet local and national needs.
24
Projected Resource Needs
George Mason has the faculty, staff, and laboratory resources required to initiate the proposed
program without compromising existing programs. The Bioengineering department already has
administrative support and office resources necessary to initiate the program.
Full-time Faculty
The Volgenau School of Engineering, mainly through the Bioengineering Department, has
committed 8 full-time faculty to the proposed program. We project that the proposed program
will initially require XX FTE of full-time faculty support, rising to XX FTE by the target year of
20XX-YY.
Part-time Faculty from Other Academic Units
Because the majority of the new courses will derive from the Volgenau School of Engineering,
we will require no part-time faculty to support the program. The marginal costs of increased
enrollment in courses outside the Volgenau School of Engineering that results from enrollment
in this program can be absorbed by the other colleges with minimal impact.
Adjunct Faculty
We project that the proposed program will not initially require any adjunct faculty. However, we
anticipate utilizing 0.XX FTE of adjunct faculty by the target year 20XX-20YY. The costs of
adjunct instruction will be accommodated through reallocation of internal resources.
Graduate Assistantships
The Office of the Provost will commit resources for one three-year Presidential Scholar’s Award
for each entering class of PhD students. By the target year, these three-year awards will support
three graduate research assistants for the PhD in Bioengineering. Presidential Scholars have a
support level of approximately $XX,XXX, which covers stipend and tuition support. In
addition, the Bioengineering Department faculty members have been routinely able to provide
support to PhD students. Since Fall of 2011, XX PhD students have been supported by
Bioengineering faculty through Graduate Assistantships. In the past, this support has gone to
graduate students in other degree programs (i.e., Electrical and Computer Engineering and
Computer Science), but if the proposed program is approved, then the support will be directed to
Bioengineering PhD students. Likewise, Bioengineering PhD students would receive high
priority for the three graduate teaching assistantships offered by the Bioengineering Department,
support which is currently directed to PhD students in other programs.
Should the proposed program be approved, the faculty plans to seek additional support for
graduate students. First, Mason has created a “Provost PhD Program Award” mechanism which
aims to enhance graduate educational programs at Mason by providing targeted funding for a
25
limited number of PhD students. Our faculty will apply for this competitive program as soon as
we are eligible to do so. Second, Bioengineering faculty will apply for PhD student training grant
programs available through the NSF and/or the NIH.
Classified Positions
We project that the proposed PhD will require no more than 0.25 FTE of classified support. The
costs of the classified position can be accommodated through an internal reallocation of
resources within the Bioengineering Department.
Targeted Financial Aid
As described above, since Fall 2011, the Bioengineering faculty has provided support to XX PhD
students through graduate research assistantships (GRAs), all from sponsored research funding.
The support for each GRA, which includes tuition, stipend, and health insurance, is
approximately $XX,XXX per year. If the proposed program is approved, future funds will be
directed to Bioengineering PhD students. In addition, three graduate teaching assistantships,
which includes tuition, stipend, and health insurance, totaling to approximately $XX,XXX per
year, will be directed to Bioengineering PhD students.
Equipment
Because no faculty or classified staff will be hired for support, the proposed program requires no
new equipment.
Library
The University Libraries system routinely commits $XXX to the purchase of research journals
and books. Appendix J documents the library resources available to support this program.
Telecommunications
No new items are required for the proposed program.
Space
No additional space is required to launch or sustain the proposed program.
Other Resources
No resources other than those described above are required to support the proposed PhD in
Bioengineering.
PROJECTED RESOURCE NEEDS FOR PROPOSED PROGRAM
Part A, Part B, Part C, Part D,
26
References
1)
Linsenmeier RA. What makes a biomedical engineer? IEEE Eng. Med. Biol. Mag. 22:
32-38, 2003.
2)
Katona PG. Biomedical engineering and The Whitaker Foundation. Annals Biomed Eng.
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30
Appendix A – Resources
Facilities:
Bioengineering Department occupies a wing of the Long and Kimmy Nguyen Building of the
Volgenau School of Engineering on the Fairfax campus. The department provides office space
for many of the Bioengineering Department faculty, offices for administrative support, a
tool/machining room, and conference room. The space is well-equipped with sufficient
computers with network access and other office-related equipment. Bioengineering Department
faculty members maintain laboratory space at either the Nguyen Engineering Building or at the
nearby Krasnow Institute of Advanced Study on the Fairfax campus.
The Blackwell laboratory encompasses 750 square feet of space, including space for
electrophysiology experiments and computing equipment. The laboratory contains lab benches;
desks; shelves and cabinets for storage of chemicals, and glassware; solution and tissue
preparation equipment; and two electrophysiology set-ups. The set-up for whole cell patch
recording has a Zeiss IR-DIC microscope, two Narishige micromanipulators, two intracellular
bridge/voltage clamp amplifiers, an ITC-16 A-D board connected to a Windows computer
running Pulse for computerized experiment control and data acquisition. The set-up for field
recording allows collecting fields from stimulated and non-stimulated control slices
simultaneously. Thus, it has two stimulators, two Warner bridge amplifiers, four mechanical
manipulators, and computerized data collection employs a NIDAC board and Labview. The
solution and tissue preparation equipment consists of a fume hood with sink, Leica vibratome, an
analytical balance, a pH meter, heating stir plate, osmometer and a refrigerator. Computing
equipment in the Blackwell laboratory includes both Windows computers and UNIX
workstations with various general purpose and special purpose software, for software
development, advanced data analysis and manuscript preparation. The PI as well as each student
and postdoc have their own multi-processor computer workstation for code development and
simulations, and we have one 16 processor workstation available for additional simulations. The
workstations also have Java and C/C++ compilers, and the neural modeling software packages,
GENESIS, Neuron and XPP. In addition to these computational resources in the CENlab, the
Krasnow Institute has a small linux cluster available at no charge to the PI.
Dr. Cebral has approximately 450 sq ft of laboratory space at the Center for Computational Fluid
Dynamics (CFD), Research Hall 1, at Mason. The laboratory is equipped with advanced graphic
workstations available to all researchers and students with the CFD Center. All faculty, research
faculty and full time students with the CFD Center are equipped with powerful workstations for
building computer models, performing medium size simulations, and visualizing results of
numerical calculations. Typical workstations have 8 to 24 Intel cores, 24 to 32 GB of RAM, and
NVIDIA graphic cards. In addition, Dr Cebral owns several data servers and compute engines
located at the Supercomputer Facility of Research Hall 1. Data servers provide a total of
approximately 40TB of redundant storage and backup. Computer servers provide approximately
a total of 148 CPU cores and 1TB of RAM memory, interconnected with a local high speed
Ethernet network. These servers are used for running big parallel high performance computations
and archiving and mining the resulting data of large numbers of models.
31
Dr. Cortes is an active faculty of the Sports Medicine Assessment, Research and Testing
(SMART) Laboratory. The SMART Lab is a brand new facility with approximately 4,000 square
feet with state-of-the-art biomechanical equipment that is well suited for motion analysis capture,
neuromuscular assessment, and ultrasound imaging research. In particular, the SMART Lab has
8 high-speed 3-Dimensional cameras (Vicon Motion Systems Ltd., Oxford, England) with 2
megapixels and 690 Hz with a unique combination of high speed, accuracy and resolution, 3
high-speed cameras with 1 megapixel resolution and 250 Hz capturing rate, 4 Bertec force plates
(Bertec Corp, Columbus OH) to quantify ground reaction forces, a wireless 8-channel
electromyography system (Delsys Trigno System) with a 2000 Hz sampling to assess muscle
activation, an Ultrasonix SonixRP clinical ultrasound system, 3 load cells/dynamometers, 1
treadmill, 1 EKG system, 1 transcranial magnetic stimulation (MagStim) with multiple coils, 2
balance plates, and 1 six degree-of-freedom electromagnetic system with 3 probes. The SMART
Lab has a quad-core 2.0 GHz Intel CPU with 8GB RAM, a secure internal server with 12TB of
storage, five laptops, two desktops, and eight iMacs are also available.
Dr. Ikonomidou’s Neuroimaging Laboratory is equipped with 5 state-of-the art Intel Xeon quadcore CPU workstations with over 10 Gb of RAM each. Two server computers are currently used
to host study data. Additional computer equipment, including four high-end laptop computers, is
also available. All computers are equipped with image processing software, including MATLAB
and IDL, and are capable of running state-of-the-art MRI processing software such as
FreeSurfer, FSL and SPM. The Neuroimaging Laboratory has access to a 3.0T Siemens
MAGNETOM Allegra head-only MRI scanner, housed at the Krasnow Institute for Advanced
Study in the Fairfax campus of George Mason University.
Dr. Jafri has approximately 600 square feet for his computational research laboratory at the
Krasnow Institute. The research laboratory houses working space for the 6 Unix workstations
and 6 Windows workstations as well as offices/desks for graduate students and postdocs. The
laboratory has a CPU/GPU cluster with 16 HP z800 workstations each with dual six-core Intel
Xeon X5650 CPUs and 24 Gbytes of memory. The cluster contains 10 NVIDIA C2050 GPUs.
Each node has 0.5 TB scratch disk space. These are connected together directly by a 40Gbps
high-speed, low latency Infiniband network. The cluster is house in the 9000 sq. ft Aquia Data
Center which has stabilized and emergency power and 24/7 staffing. In addition, the Jafri Lab
has two HP z820 Workstations with dual quad-core Intel Xeon E5-2643 processors and 32
Gbytes of memory. One of these acts as a file server with 6 TB user disk space. The other is
equipped with a NVIDIA K20 GPU. Also in the lab is a HP z800 workstation with dual quadcore Xeon Nehalem CPUs and 18 Gbytes memory, a Super Micro dual quad-core Xeon
Westmere system with 24 Gbytes memory, and two dual processor dual-core AMD Opteron HP
XW9300 Linux Workstations. In these are installed 3 NVIDIA C1060 GPUs, 3 NVIDIA
GTX480 GPUs, and one NVIDIA GTX470 GPU. All these workstations are running the Ubuntu
Linux operating system equipped with Portland Group, Inc. Fortran/CUDA FORTRAN
compliers MATLAB, IDL, IMAGEJ and other essential software. There are 5 HP Elitebook
Mobile Workstations and and 1 HP XW4100 workstation assigned to lab members running
Microsoft Windows.These are all connected to each other and the Internet by gigabit ethernet.
Files are mirrored on a 12 TB Seagate Black Armor network Raid system and backed
up/archived with a HP 448 ultrium tape system.
32
Dr. Sikdar has approximately 850 sq ft of laboratory space at the Krasnow Institute for Advanced
Study at Mason. The laboratory is well-equipped for ultrasound imaging research, both
experimental as well as on human subjects. The laboratory maintains the following equipment:
Ultrasonix SonixRP clinical ultrasound system with high frequency 5-14 MHz and real-time 3D
imaging probes, Terason T3000 portable laptop-based ultrasound system with multiple
transducers, Verasonics 64-channel ultrasound data acquisition system, Spencer Technologies
ST3 Transcranial Doppler instrument, equipped with 2-MHz diagnostic probe and two 2-MHz
bilateral monitoring probes, Interson SeeMore USB-based portable ultrasound probe for mobile
applications, 3DGuidance TrakStar magnetic position sensing system with four six-degree of
freedom position tracking sensors (Ascension technologies), ISS OxiplexTS near infrared
spectrophotometer, a secure high-performance (quad core 2.0 GHz Intel CPU with 4GB RAM)
RAID file server with 2 Terabytes of data storage capacity, a dual quad-core 2.26 GHz Intel
Xeon system with 12GB 1066-MHz DDR3 RAM, and a dual 2.66 MHz hex-core MacPro server
with 12 GB RAM.
Dr. Shehu’s Computational Biology Lab consists of 5 workstations in the Artificial Intelligence
Laboratory on the 4th floor of the Nguyen Engineering building. In addition, equipment is
available from the Department of Computer Science: A departmental computing cluster is
available for computational projects for undergraduates, containing 53 Intel Xeon 1U rackmount
units of which half are 1-Core 2-CPU and half are 2-Core 2 2-CPU connected by 1 GB copper
ethernet. Each unit has 700 GB shared storage and 1GB RAM per core. A 7.3TB XServe storage
device is associated with the cluster.
Drs. Peixoto and Pancrazio share Neural Engineering laboratory space on the 3rd floor of the
Engineering Building. The laboratory consists of two fully outfitted wet labs comprising 700 sq
feet with chemical hoods, dedicated electrophysiology, chemical solution preparation, and
electrochemistry workstations. In addition, between the two laboratories is a shared 200 sq ft cell
culture facility which is fully operational. Major equipment includes an Omniplex multichannel
data acquisition system (Plexon Inc), Axopatch 200 patch clamp amplifier (Axon Instruments),
PatchStar Micromanipulator (Scientifica), Digidata/pCLAMP data acquisition system (Axon
Instruments), PC-10 pipette puller (Narishige), CPM-2 Coating and Polishing microforge (ALA
Scientific Instruments), potentiostat (Gamry), two multichannel electrochemical station
(CHinstruments), dual stack CO2 incubators, laminar flow biosafety cabinet, -80 C freezer,
general use refrigerator, centrifuge, in vitro microelectrode array system (Multichannel Systems),
three inverted microscopes equipped for fluorescence immunohistochemistry (Nikon Eclipse),
phase contrast/DIC imaging for patch clamp experiments (Zeiss Axio-Observer), and a trinocular
inverted microscope for cell culture use (World Precision Instruments).
Dr. Salvador Morales has approximately 500 square feet of wet lab space at the Krasnow
Institute for Advanced Study at Mason. The laboratory, which is dedicated to nanomedicine and
nanotechnology, has the following equipment: high shear mixer, centrifuge, bench top
centrifuge, analytical balance, Nanodrop UV-Vis spectrophotometer (Thermo Scientific), 3
micro-centrifuges, speed “VAC” vacuum concentrator, pH meter, freezer, hot-plates, and minirefrigerator.
33
Dr. Rangwala has access to 2000 sq. ft of laboratory space on the 4th floor of the Engineering
Building with the following computer equipment: Computer Science department Beowulf
cluster with 64 dual-core and quad-core Intel PCs connected via fast Ethernet switch, 5
workstations consisting of dual-core Intel Pentium at 2.6GHz, 4GB memory. All computers are
running Linux and have a number of software packages installed on them including Matlab,
Oracle, mySQL, and GNU development tools. Dr. Rangwala’s Data Mining Laboratory has
workstation servers with 12 dual-core and quad-core Intel PCs. Two of these servers have 32 GB
RAM each, and also have nVidia gifted Tesla and Quadro cards.
Dr. Joiner has approximately 600 square feet of laboratory space on the second floor of the The
Volgenau School of Engineering at Mason. The focus of the laboratory is studying sensorimotor
integration and motor control through experimental and computational approaches. In addition to
six pentium class computer workstations for analysis and computational modeling, the
laboratory's main experimental equipment is a two arm robotic manipulandum (KINARM). This
end-point robot is a stiff, graspable robot that can create highly complex mechanical
environments. In addition, the setup has a 2D virtual reality display for natural, intuitive
presentation of visual stimuli.
Dr. Wei has approximately 160 square feet of lab space in the Volgenau School of Engineering
at Mason. The laboratory, which is dedicated to computational biomechanics, is equipped with
high performance computers as well as a binocular, infrared, head-mounted eye tracker.
Dr. Agrawal maintains approximately 480 sq ft of wet laboratory space at the Krasnow Institute
for Advanced Study at Mason. The laboratory is already equipped with a chemical fume hood,
house vacuum and compressed air connections as well as an additional available line for
supplying other gases if required. Within the laboratory, dedicated bench spaces have been
assigned for device fabrication and tissue culture work. The laboratory contains the following
equipment to carryout microfluidics and cell biology research: AMG EVOS FL Auto fluorescent
microscope with integrated monochrome and color cameras, automated stage and LED light
source for live cell imaging; AMG EVOS XL microscope with integrated LCD and time lapse
imaging capability; 4 ft. Esco Labculture Class II Type A2 biological safety cabinet; cell and
particle counter (Beckmen coulter counter Z2); microplate reader (Spectramax Gemini EM,
Molecular Devices); a plasma system (PE-50, Plasma Etch Inc); Laurell programmable spin
coater (WS-650-23); two lab ovens (Quincy labs, model 10AP); programmable syringe pump
(Chemyx, Fusion 200); -86C freezer (REVCO Ultima II, 21 cu.ft.); and refrigerated centrifuge
(Eppendorf 5417R); non-refrigerated microcentrifuge (Eppendorf 5415 D); Thermo IEC Centra
CL3 bench top centrifuge, dual chamber water bath (Thermo Scientific Isotemp 215); two vortex
mixers (Vortex Genie 2, Scientific Industries); Branson 5510 sonication bath; 25L cryotank,
VWR hot plate stirrer, 21 cu.ft. top freezer refrigerator; pH/conductivity meter (Fisher Scientific
Accumet AB200); and a thermal printer (Zebra TLP-2844Z). In addition, Dr. Agrawal is
developing a 235 sq. ft. clean room (Class 1000) microfabrication facility within the Krasnow
Institute to facilitate advanced microfluidics research at the university.
34
Appendix B – Catalog Description of Courses
(* denotes new course to be developed and implemented for the proposed program)
*BENG 501 - Bioengineering Research Methods
Credits: 3(NR) Examines approaches for scientific research with applicability to research in
bioengineering. Topics include biophysical origins of bioengineering measures, tools and
technology for bioengineering data collection, basic principles of experimental design and
statistical analyses, and interpretation of scientific results.
Prerequisite: Graduate Standing
BENG 525 - Neural Engineering
Credits: 3 (NR) Provides an overview of topics in Neural Engineering. Topics covered range
from sensory and motor prosthetic devices, stimulation of biological tissue, bioelectrodes and
characterization techniques, brain-machine interfaces, and engineered devices to ameliorate
neurological disorders.
Prerequisite(s): Graduate Standing or permission of instructor.
BENG 538 - Medical Imaging
Credits: 3 (NR) Provides an introduction to the physical, mathematical and engineering
foundations of modern medical imaging systems, medical image processing and analysis
methods. In addition, this course introduces engineering students to clinical applications of
medical imaging. The emphasis is on diagnostic ultrasound and magnetic resonance imaging
methods, although several other modalities are covered. The course also provides an overview of
recent developments and future trends in the field of medical imaging, discusses some of the
challenges and controversies, and involves hands-on experience applying the methods learnt in
class to real-world problems.
Equivalent to ECE 538
Prerequisite(s): Graduate Standing or permission of instructor; ECE 320 or BENG 320; PHYS
262 or equivalent.
*BENG 541 – Biomaterials
Credits: 3(NR) This course covers the principles of biomaterials and biological interactions with
materials, including an overview of biomaterials characterization, design and testing. The
emphasis of this course will be on emerging strategies and design considerations of biomaterials.
Specific areas of concentration will include the use of polymers, ceramics and metallics in
biomaterials, drug delivery applications, tissue engineering from an orthopedic and vascular
perspective, biocompatibility and acute and chronic biological response to implanted material. In
vitro and in vivo testing of biomaterials will also be covered in this course.
Prerequisite(s): Graduate Standing or permission of instructor.
*BENG 551 - Translational Bioengineering
Credits: 3(NR) Demonstrates the process for creation of both medical devices and companies in
the medical device field. This course focuses on teaching students to design and build up a robust
medical device prototype, write a business plan, and present a company vision. This course will
35
draw upon lectures, videos and four different guest speakers who are co-founders of successful
biomedical startup companies.
Prerequisite(s): Graduate Standing or permission of instructor.
BENG 636 - Advanced Biomedical Signal Processing
Credits: 3 (NR) Provides an overview of advanced topics in biomedical signal processing with an
emphasis on practical applications. Topics include introduction to physiological origins of
biomedical signals, stochastic and adaptive signal processing, spectral estimation, signal
modeling and analysis of nonstationary signals.
Prerequisite(s): Graduate Standing; ECE 535 or equivalent; ECE 528 or equivalent.
*BENG 725 - Computational Motor Control
Credits: 3(NR) This course uses approaches from robotics, control theory, and neuroscience to
understand biological motor systems. The focus of the course is modeling muscles, reflexes and
neural systems in order to understand how the central nervous system plans and controls
movement of the eyes and limbs.
Prerequisite(s): Graduate Standing; BENG 525
*BENG 738 - Advanced Medical Image Processing
Credits: 3(NR) This course covers advanced processing techniques used in modern medical
imaging. This course focuses primarily on neuroimaging analysis techniques, however
fundamental concepts such as segmentation or registration which are applicable to imaging of
other body regions are also covered. The course aims at developing an understanding of the
mathematical background, principles and application of techniques such as segmentation,
registration, morphometry, general linear modeling, and principal/independent component
analysis.
Prerequisite(s): XXXXXX
*BENG 641 - Advanced Nanotechnology in Health
Credits: 3(NR) This course provides interdisciplinary scientific and engineering approaches to
solve relevant medical problems. The course is divided in two main sections. In the first section,
students will learn polymer structure and composition, polymer material properties and different
types of natural and synthetic polymers. In the second part, students will apply this knowledge to
design novel nanocarriers for controlled drug release, scaffolds for tissue engineering
applications and new vectors for vaccines. In addition, in this course, students will have the
opportunity to discuss in depth the relevance of Nanotechnology to advance medical treatments
in cancer, infectious and neurodegenerative diseases.
Prerequisite(s): Graduate Standing; CHEM 441, BIOL 682, or permission of instructor
*BENG 650 - Advanced Biomechanics
Credits: 3(NR) Introduces the fundamental concepts of musculoskeletal biomechanics, and how
to apply mechanical principles to quantitatively describe, analyze, and model movement. Topics
include properties, functions, and models of the musculoskeletal structures, 3D kinematics,
forward and inverse dynamics as well as instrumentation systems applied in movement analysis.
Students are required to complete a project that could be either a critical review of a topic or an
implementation.
36
Prerequisite(s): Graduate Standing; BENG 501, RHBS 710
*BENG 745 - Biomedical Systems and Microdevices
Credits: 3(NR) Bio-micro-electro-mechanical systems (BioMEMS) provide a robust approach to
mimic in vivo microenvironments within controlled in vitro settings. The goal of this course is to
introduce students with the concepts of highly interdisciplinary field of Lab-on-a-Chip
technologies with emphasis on its advanced applications in biological and biomedical
engineering. In addition to the microfabrication processes, a variety of analytical techniques
routinely used in biomedical research will also be covered.
Prerequisite(s): Graduate Standing; CSI 720, or permission of instructor
*BENG 750 - Modeling and Simulation of Human Movement
Credits: 3(NR) Introduces the development, simulation, and characterization of data-driven 3D
neuro-musculoskeletal models that can be used to quantitatively explore human movement in
health and disease. Topics include reconstructing 3D geometric models of bones and muscles
from medical imaging data, estimating joint kinematics from motion capture data, creating
simulations of musculoskeletal motion incorporating multimodality data, and analyzing muscle
and joint forces for static and dynamic activities applying computational tools. Students will
learn and use open source computational biomechanics software to simulate movement. The
course consists of lectures, student paper presentations, and computer laboratories. A semesterlong research project is required.
Prerequisites: Graduate Standing; BENG 538, BENG 650
*BENG 820 - Seminar in Neuroengineering
Credits: 3 (RD) Selective analysis and discussion of topics in neuroengineering in areas of
current research interest. Topics may include brain machine interfaces, advanced materials for
implantable devices, computational neuroscience, neuronal biosensors and assays, and
neuroprosthetics.
Prerequisite(s): Graduate Standing; BENG 525
*BENG 830 - Seminar in Biomedical Imaging
Credits: 3 (RD) Selective analysis and discussion of topics in biomedical imaging in areas of
current research interest. Topics may include techniques and analyses for ultrasound, magnetic
resonance imaging (MRI), functional MRI, nuclear imaging, computer assisted tomography,
positron emission tomography, and emergent approaches to imaging for health and disease.
Prerequisite(s): Graduate Standing; BENG 538
*BENG 840 - Seminar in Nano-scale Bioengineering
Credits: 3 (RD) Selective analysis and discussion of topics in nano-scale bioengineering in areas
of current research interest. Topics may include nanoengineered materials, nanoscale devices and
systems, and novel nano-scale fabrication and modeling approaches with application to
biomedicine.
Prerequisite(s): BENG XXX
*BENG 850 - Seminar in Biomechanics
37
Credits: 3 (RD) Selective analysis and discussion of topics in biomechanics in areas of current
research interest. Topics may include computational and physiological modeling for
biomechanics, multiscale representation of biomechanical systems, data fusion techniques for
biomechanics, and application of quantitative biomechanics for diagnostics or medical
intervention.
Prerequisite(s): BENG 650
*BENG 998 - Doctoral Dissertation Proposal
Credits: 1-12 (RD) Work on research proposal that forms basis for doctoral dissertation. Notes:
May be repeated. No more than 24 credits of BENG 998 and 999 may be applied to doctoral
degree requirements.
*BENG 999 - Doctoral Dissertation
Credits: 1-12 (RD) Formal record of commitment to doctoral dissertation research under
direction of BENG faculty member.
Prerequisite(s): Admission to candidacy. Notes: May be repeated as needed. Students must
complete minimum 12 credits of doctoral proposal (BENG 998) and doctoral dissertation
research (BENG 999) Maximum of 24 credits of BENG 998 and 999 may be applied to degree.
Students who choose to take less than 24 credits of BENG 998 and 999 may earn remaining
credits from approved course work. Students cannot enroll in BENG 999 before research
proposal accepted and approved by dissertation committee.
38
Appendix C – Sample Schedules for PhD in Bioengineering
Notes:
1. For sake of simplicity, one of the four concentrations (neuroengineering) is used to
illustrate the sample schedule permutations.
2. TQE indicates timing of the technical qualifying exam; RQE indicates timing of the
research qualifying exam
Sample Student Schedule for a Full-Time Post-Baccalaureate Student
Year
Fall
Spring
Yearly Credits
Cumulative
Year 1
BENG 501 (a)
BENG 551 (a)
18
18
ECE 528 (b)
ECE 535 (b)
RHBS 710 (b)
BENG 525 (c)
NEUR 602 (c)
BENG 725 (c)
18
36
ECE 521 (d)
ECE 620 (d)
CS 580 (d)
ECE 621 (d)
TQE
RQE
BENG 998
BENG 998
12
48
ECE 722 (d)
ECE 738 (e)
BENG 998
BENG 998
12
60
CS 688 (e)
BENG 820 (e)
12
72
Year 2
Year 3
Year 4
Advance to
candidacy
Year 5
BENG 999
BENG 999
(a) = required bioengineering core class; (b) fulfills mathematics and bioscience requirements; (c) required
concentration course; (d) = fulfills technical elective requirements in engineering; (e) fulfills concentration elective
requirements. All courses are 3 credits except for BENG 999 (6 credit hrs).
Sample Student Schedule for Part-Time Post-Baccalaureate Student
Sample Student Schedule for Full-Time Post-Master’s Student
Sample Student Schedule for Part-Time Post-Master’s Student
39
Appendix D- Abbreviated CV’s for the PhD Faculty
Volgenau School of Engineering Bioengineering Graduate Program Faculty
*Nitin Agrawal, Assistant Professor, Department of Bioengineering. PhD: 2006, Chemical
Engineering, Texas A&M University. Postdoctoral fellow: 2006-2009, Harvard Medical School,
MGH Hospital and Shriner’s Burn Hospital. Research interest: cell migration, microfluidics.
Kenneth Ball, Dean, Volgenau School of Engineering. LS Randolph Professor and Head.
Department of Mechanical Engineering, Virginia Polytechnic Institute and State University.
PhD: 1987, Mechanical Engineering, Drexel University. Research interests: computational fluid
dynamics, heat transfer in turbulent flow with applications to biomedicine.
Kim “Avrama” Blackwell, Krasnow Professor of Computational Neuroscience and
Neurophysiology, College of Science, The Krasnow Institute for Advanced Studies. PhD: 1988,
Bioengineering, University of Pennsylvania. Research interests: biophysical and biochemical
mechanisms of long term memory storage studied through computational and
electrophysiological techniques.
*Caitlin Burke, Assistant Professor (instructional) and Acting Associate Chair, Department of
Bioengineering. PhD: 2011, Biomedical Engineering, University of Virginia. Postdoctoral
fellow: 2011-2012, NIH. Interests: ultrasound, thermo-chemo-radiotherapy, bioengineering
education.
Juan Cebral, Professor, School of Physics, Astronomy and Computational Sciences. PhD: 1996,
Computational Sciences and Informatics, George Mason University. Research interests:
computational fluid dynamics, modeling of blood flow, applications to cerebral aneurysms.
Nelson Cortes, Assistant Professor, Division of Health and Human Performance, College of
Education and Human Development. PhD: 2010, Human Movement Sciences, Old Dominion
University. Research interests: biomechanics, muscle dynamics, injury prevention.
Kenneth De Jong, Professor, Department of Computer Science, Volgenau School of
Engineering; Associate Director, The Krasnow Institute for Advanced Studies. PhD: 1975,
Computer Science, University of Michigan. Research interests: genetic algorithms, machine
learning, artificial intelligence.
*Vasiliki N. Ikonomidou, Assistant Professor, Department of Bioengineering. PhD: 2002,
Electrical and Computer Engineering. Visiting Fellow and then Research Fellow: 2003-2009,
Neuroimmunology Branch, NINDS/NIH. Research interest: magnetic resonance imaging.
M. Saleet Jafri, Professor, School of Systems Biology, College of Science; Department of
Molecular Neuroscience, The Krasnow Institute for Advanced Studies. Professor and Chair:
Department of Bioinformatics and Computational Biology. PhD: 1993, Biomedical Sciences,
The City University of New York. Research interests: mathematical modeling of the
cardiovascular system, cell signaling, cell energetics.
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*Wilsaan Joiner, Assistant Professor of Bioengineering. PhD: 2007, Biomedical Engineering,
Johns Hopkins University. Postdoctoral fellow: 2007-2012, NIH. Research interests: eye
movement, perception.
Peter Katona, Professor (part time, since 2006), Department of Electrical and Computer
Engineering. PhD: 1965, Electrical Engineering, MIT. Assoc. Prof, Prof, Chair: Biomedical
Engineering, Case Western Reserve University 1969-90. VP, President, The Whitaker
Foundation 1991-2006.
Dmitri Klimov, Associate Professor, School of Systems Biology, College of Science. PhD: 1992,
Physics, Moscow State University. Research interests: computer simulation of biomolecules,
formation of amyloid fibrils, structural transitions in proteins due to mechanical forces.
*Joseph Pancrazio, Professor and Chair, Department of Bioengineering. PhD: 1990, Biomedical
Engineering, University of Virginia. Research Engineer, then Head, Laboratory of Biomolecular
Dynamics, Naval Research Laboratory 1998-2004. Program Director, Neural Engineering,
NIH/NINDS 2004-09. Research interest: neural interfaces and next generation neuron-based
assays.
Nathalia Peixoto, Associate Professor, Department of Electrical and Computer Engineering.
Affiliate appointment in Department of Bioengineering. PhD: 2001, Electrical Engineering.
Postdoctoral Fellow: 2001-2002, Stanford; 2003-2006, Center for Neural Dynamics, George
Mason University. Research interest: electrical activity of the brain.
Huzefa Rangwala, Assistant Professor (since 2008), Department of Computer Science. Affiliate
appointment in Department of Bioengineering. PhD: 2008, Computer Science, University of
Minnesota. Research interest: bioinformatics, machine learning.
*Carolina Salvador Morales, Assistant Professor, Department of Bioengineering. PhD: 2007,
Chemistry, University of Oxford. Postdoctoral fellow: 2007-2011, MIT. Interests: nanoparticles,
immunology, entrepreneurship.
Padmanabhan Seshaiyer, Professor, Department of Mathematical Sciences, College of Science.
PhD: 1998, Applied Mathematics, University of Maryland, Baltimore County. Research
interests: advanced scientific and parallel computing, computational biomechanics.
Amarda Shehu, Assistant Professor, Department of Computer Science. Affiliate appointment in
Department of Bioengineering. PhD: 2008, Computer Science, Rice University. Research
interest: protein structure.
*Siddhartha Sikdar, Assistant Professor, Department of Bioengineering. Joint appointment in the
Electrical and Computer Engineering Department. PhD: 2005, Electrical Engineering, University
of Washington. Postdoctoral fellow: 2005-2008, Department of Bioengineering, University of
Washington. Research interest: medical ultrasound.
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James Thompson, Associate Professor, Department of Psychology, College of Science. PhD:
20??, Psychology, Swinburne University, Australia. Postdoctoral Fellow: Department of
Radiology, West Virginia University, 20yy- zz. Research interests: neural basis of perception of
human actions using behavior, fMRI and EEG.
Iosif Vaisman, Professor and Associate Director, School of Systems Biology, College of
Science. PhD: 1990, Physical Chemistry, USSR Academy of Science. Research interests:
computational methods for determining protein structure and function.
*Qi Wei, Assistant Professor, Department of Bioengineering. PhD: 2010, Computer Science,
Rutgers University. Postdoctoral fellow: 2010-2012 Northwestern University. Research interests:
computational biomechanics, neuromuscular systems, motor control.
* Core Bioengineering Department Faculty
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Appendix E – Departmental Faculty Research
Active Funded Projects:
COLLABORATIVE RESEARCH: Spatial and Temporal Aspects of cAMP/PKA Signaling
Underlying Information Processing in Neurons
Submitted to joint NIH-NSF program on Collaborative Research in Computational
Neuroscience, Grant awarded by NIAAA
Budget: $1,191,000
Dates: 2008-2013
Bioengineering Affiliate Faculty: Kim Blackwell (PI)
From Attentive to Automatic Performance: A Multi-Scale, Multi-Species, and Multi-Modal
Investigation of Spatial Learning
MURI grant from ONR
Budget: $7,476,164
Dates: 2009-2014
Bioengineering Affiliate Faculty: Kim Blackwell (co-PI)
Computational Analysis of Cerebral Aneurysm Evolution
Funding Agency: NIH
Budget: $1,664,453
Dates: 09/01/2007-5/31/2013
Bioengineering Affiliate Faculty: Juan R. Cebral (PI)
The goal of this project is to study the hemodynamics in unruptured cerebral aneurysms followed
with non-invasive imaging to better understand the role of hemodynamics in the process of
aneurysm progression, and to test whether hemodynamics can be used to improve the risk
assessment of intracranial aneurysms.
Computational and Biological Approach to Flow Diversion
Funding Agency: NIH
Budget: $895,395
Dates: 9/1/2011-8/31/2016
Bioengineering Affiliate Faculty: Juan R. Cebral (Co-I)
In this project, anatomically and physiologically accurate computational models of cerebral
aneurysms are constructed from multimodality images acquired in animal models treated with
flow diverting stents. These models are then used to identify the hemodynamic conditions that
predispose different aneurysms and different regions of the aneurysms to thrombose after
implantation of flow diverting devices. This study seeks to better understand how flow diverting
devices work and subsequently improve endovascular interventions.
Evaluation of Flow Diversion Treatment for Cerebral Aneurysms
Funding Agency: NIH
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Budget: $80,000
Dates: 2010-2013
Bioengineering Affiliate Faculty: Juan R. Cebral (PI)
This project uses patient-specific computer models of cerebral aneurysms to evaluate the
prognostic value of average blood velocity changes before and after treatment with flow
diverting devices. Additionally, this project seeks to develop efficient methods for modeling
blood flows in stented cerebral aneurysms using a porous media approach that could be used for
treatment planning in routine clinical practice.
Hemodynamics in Intracranial Aneurysm Pathogenesis
Funding Agency: NIH
Budget: $398,709
Dates: 08/01/2011-7/31/2013
Bioengineering Affiliate Faculty: Juan R. Cebral (Co-PI)
The overall goal of this project is to identify hemodynamic conditions associated to the
formation of cerebral aneurysms. For this purpose patient-specific image-based computational
fluid dynamics of intracranial aneurysms are used to approximate the in vivo hemodynamic
conditions at known locations in cerebral arteries where aneurysms later developed.
The link between hemodynamics and wall structure in cerebral aneurysms
Funding Agency: NIH
Budget: $423,851
Dates: 04/01/2013-3/31/2015
Bioengineering Affiliate Faculty: Juan R. Cebral (Co-PI)
This project combines information from numerical simulations, multi-photon microscopy and
mechanical tissue testing to determine the detail interactions between the blood flow, aneurysm
wall structure and its mechanical strength. For this purpose, patient-specific computational
models are constructed from medical images and used to represent the aneurysm hemodynamics.
Tissue samples are harvested during neurosurgery and analyzed with multi-photon microscopy to
determine the wall structure (cellular content, collagen fiber organization), and subsequently
tested under loading conditions to determine the mechanical strength of the wall. This project
will shed light into the mechanisms responsible for aneurysm development, progression and
ultimately rupture.
Functional Evaluation to Predict Lower Extremity Musculoskeletal Injury
Funding Agency: National Institute of Health (NIAMS)
Budget: $1,952,090
Dates: 2012-2017
Bioengineering Affiliate Faculty: Nelson Cortes (Co-I)
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The proposed research is highly relevant to public health because the health benefits associated
with physical activity for children are significant and well reported. Yet the millions of
individuals of all ages who engage in recreational exercise and sport face potential short and long
term limitations to their function and participation across the lifespan due to lower extremity
musculoskeletal injury. Thus, the proposed research is consistent with NIH mission that pertains
to the development of fundamental knowledge to extend healthy life and develop translational
clinical research to help reduce the burdens of injury and disability.
Calcium Entrained Arrhythmias
Funding Agency: National Institutes of Health (R01HL105239-02)
Budget: $1,258,236 (GMU portion of a total of $5,509,890.44 Multi-PI Project)
Dates: 2011-2015
Bioengineering Affiliate Faculty: M. Saleet Jafri (PI)
The project integrates experiments and multiscale modeling to gain a systems level
understanding of the molecular and cellular events that lead to cardiac arrhythmias that are due
to a defect in cardiac calcium dynamics.
CUDA Teaching Center/CUDA Research Center
Funding Agency: NVIDIA
Budget: $12,000
Dates: 2011-2013
Bioengineering Affiliate Faculty: M. Saleet Jafri (PI)
The teaching center award was received for efforts to instruct (mentoring and courses) students
how to program in the CUDA programming language that utilized modern GPUs (graphical
processing units) for computation. The research center award was received for the application of
CUDA technologies for scientific research.
Role of the Saccadic Eye-movement Corollary Discharge in Stable Visual Perception.
Funding Agency: National Institutes of Health (R00 - NIH Pathway to Independence Award)
Budget: $746,461
Dates: 2012-2015
Bioengineering Faculty: Wilsaan Joiner (PI)
Stable visual perception is maintained despite the frequent saccadic eye-movements that disrupt
the visual input. One hypothesis for this compensation is that advanced knowledge of the
impending saccade is provided by an internal copy, a corollary discharge (CD) signal, of the
motor command. This copy is utilized to distinguish self-generated sensations from
environmental disturbances and reflects properties of the original motor command. Examining
normal human subjects and patients with Schizophrenia, the goal of this project is to understand
the role of corollary discharge in visual perception and movement control with the objective of
contributing to the diagnosis and treatment of diseases that result from CD transmission deficits.
The expected outcomes of the research is to provide evidence that perceptual judgments of transsaccadic changes is influenced by the variability reflected in the CD signal, and that this
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detection ability is diminished when CD transmission is degraded in Schizophrenia. This
contribution is significant because in addition to diagnostics, the majority of the internal signals
in the brain that do not represent sensory or motor information are presently inaccessible, and it
is likely that diseases that impair higher cognitive function affect these circuits, as in
Schizophrenia. CD is one of the few internal signals that is experimentally accessible and,
through its study, an enhanced understanding of these signals and their transmission can be
achieved.
Biocompatibility of Advanced Materials for Brain Machine Interfaces (BAMBI)
Funding Agency: Defense Advanced Research Projects Agency
Budget: $3,234,626
Dates 2011-2014:
Bioengineering Faculty: Joseph J Pancrazio (PI) and Nathalia Peixoto (co-PI)
The overall goal of this project is to establish and demonstrate a systematic process for testing
novel materials for use in a new generation of reliable brain machine interfaces (BMIs).
Biomaterial testing involves three fundamental phases: 1) in vitro testing of the material with cell
lines; 2) material robustness and durability; and 3) in vivo testing. This project will fill a critical
gap in our ability to take full advantage of emerging materials by supplying this testing
capability to the material science community and working cooperatively to bring new materials
forward. Our approach is novel since it will enable a translational bridge for exciting
developments in material science to be rapidly considered for neural interface applications. Our
group provides multidisciplinary access to expertise in electrochemistry, material science, in
vitro cellular assays, and immunohistochemistry to conduct consistent, rapid, and systematic
assessments of novel materials.
GARDE: Equals: Enhancing Quality of Life of Students through Senior Designs
Funding Agency: NSF
Budget: $125,000
Dates: 2012-2016
Bioengineering Faculty: Nathalia Peixoto (PI); Vasiliki Ikonomidou (co-PI)
Intellectual merit: The main intellectual merit of this project is the design of devices that enhance
the quality of life of students with disabilities. So far one device has been delivered, and three
other devices are currently being developed. Results from one project have been presented at the
Rehabilitation Engineering and Assistive Technology Society of North America conference, and
the design team was selected as one of the ten finalists. Broader impacts: we have so far trained
15 senior engineering design students. Even in this early phase, the senior design projects are
attracting attention from the media, both internal and external to GMU. One of the projects
involved the development of an automated camera control system. This project sparked a
collaboration between the engineering students and Business School students, and a company
was founded to explore potential commercialization of the system they developed. The company
took part in the GMU business plan competition and their project was selected as one of the ten
finalists.
Pattern-Steering in Nonlinear Dynamical Networks
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Funding Agency: NSF
Budget: $300,000
Dates: 2010-2013
Bioengineering Affiliate Faculty: Nathalia Peixoto (co-PI)
This project makes use of several mathematical models to understand and to explain the
dynamics in two experimental models of complex networks: neuronal cell cultures and nematic
liquid crystals. In this project we have worked toward steering those networks from one basin to
another, associating the mathematical models to experimental patterns.
Cortical Stimulation for Seizure Disruption in a Rodent Epilepsy Model
Funding Agency: Mason/INOVA Life Sciences Research Grant
Budget: $50,000
Dates: 2013-2014
Bioengineering Affiliate Faculty: Nathalia Peixoto (PI), James Leiphart (co-PI)
Seizures may be halted through electrical stimulation of the seizure focus. This project explores
sub-threshold stimulation waveforms that yield a lower seizure rate in the kainic acid model of
epilepsy in rodents.
VA STEM CoNNECT: Virginia Collaborative Nurturing Network to Enhance Content-Focused
Teaching.
Funding Agency: VA Dept of Education
Budget: $100,000
Dates: 2011-2013
Bioengineering Affiliate Faculty: Nathalia Peixoto (co-PI)
This grant supports the development of engineering design lectures to math and science teachers
in Middle and High School in Virginia. The objective is to strengthen the connection between
mathematics and engineering early on in the educational system.
MRI: Acquisition of Electron Beam Evaporation System for Multidisciplinary Research and
Education
Funding Agency: NSF
Budget: $300,000
Dates: 2011-2013
Bioengineering Affiliate Faculty: Nathalia Peixoto (co-PI)
This major research instrumentation grant supports the acquisition of an evaporator that can
deposit and define thin films of conductive materials. The specific research interest for the
Neural Engineering Lab is to design implantable probes and fabricate them in house.
Pathogenesis and Pathophysiological Mechanisms of Myofascial Trigger Points
Funding Agency: National Institutes of Health (R01)
Budget: $2,193,086
Dates: 2010-2014
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Bioengineering Faculty: Siddartha Sikdar (PI)
The specific aims of this project are designed to understand the relationship between MTrPs and
chronic pain. Myofascial pain syndrome (MPS) is a common, soft-tissue musculoskeletal
disorder, which can be characterized by MTrPs. This investigation will study two groups: (A)
patients with chronic soft-tissue neck pian and palpable MTrPs (N=90) and (B) asymptomatic
normal subjects (N=30). New and innovative methodology combining ultrasound, mechanical
imaging, biochemical techniques and physical examination measures to quantitatively
characterize MTrPs. Using ultrasound imaging and biochemical assays, information about the
soft tissue and vascular environments associated with MTrPs and without MTrPs will be
assessed. Subjects in group A with MTrPs will be treated by perturbing trigger points with a 3week course of dry needle therapy, and these subjects will be re-evaluated clinically and using
imaging and biochemical measurements, immediately after the treatment and at 3 weeks follow
up. The overall hypothesis is the pathogensis of MPS involves local trauma to the muscle fibers
producing high levels of pro-inflammatory cytokines and nociceptive) pain-inducing) substances.
The altered biochemical milieu causes sustained muscle contracture leading to blood vessel
compression and local ischemia, resulting in the painful nodule (MTrP). Relieving the module
through dry needle therapy restores blood flow and the biochemical milieu.
CAREER: An Integrated Systems Approach to Understanding Complex Muscle Disorders
Funding Agency: National Science Foundation
Budget: $400,000
Dates: 2010-2015
Bioengineering Faculty: Siddartha Sikdar (PI)
The objective of this research is to investigate complex dynamic interactions between the
musculoskeletal, circulatory and nervous systems involved in common, yet poorly understood,
muscle disorders. The approach is to develop novel dynamic ultrasound imaging modes for
quantifying anisotropic muscle kinematics, viscoelastic tissue properties and blood flow, and
integrate these novel measures with conventional measures of tissue oxygenation, electrical
activation, strength, and range of motion to characterize the underlying physiological systems.
Intellectual Merit: Real-time ultrasound imaging is uniquely suited for dynamic muscle function
studies because it is cost-effective, portable and can be integrated with other measurements.
However, the lack of quantitative dynamic measures and challenges due to anisotropy of muscle
has resulted in barriers to widespread use of ultrasound. The proposed research is designed to
overcome these barriers. The technical contributions are the theoretical and experimental
investigation of novel ultrasound beam configurations, imaging modes and signal and image
processing algorithms for quantitative imaging of anisotropic tissue motion and viscoelastic
tissue properties. Broader Impacts: This research will provide enabling tools for understanding
functional limitations in musculoskeletal disorders and measuring treatment efficacy, potentially
leading to more effective therapies for this significant public health problem. Research and
educational objectives are integrated to engage graduate, undergraduate and high school students
as part of a new bioengineering curriculum. Outreach efforts include summer research programs
for high school students, a bioengineering demonstration kit encouraging students to pursue
careers in science and engineering, and engaging the local K-12 community by presenting stateof-the-art research on muscle disorders affecting school-age children
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Asymptomatic Carotid Stenosis: Cognitive Function and Plaque Correlates (ACCOF)
Funding Agency: Department of Veterans Affairs
Budget: $247,930
Dates: 2011-2012
Bioengineering Faculty: Siddartha Sikdar (PI)
Carotid artery plaques are known to cause stroke. Cognitive impairment is an insidious but
poorly understood problem in patients with carotid plaques. In this study, we are uncovering the
extent of cognitive impairment in veterans with carotid stenosis who are currently labeled
"asymptomatic". We are using sophisticated 3D imaging techniques developed by our group to
measure the structure and composition of plaques, number of particles breaking off from them,
and blood flow restriction to the brain from them. This will help identify patients at risk for
cognitive impairment who may benefit from preventative measures and improve selection of
patients to decrease unnecessary surgical procedures.
III: Medium: Collaborative Research: Computational Methods to Advance Chemical Genetics by
Bridging Chemical and Biological Spaces
Funding Agency: NSF
Budget: $339,537
Dates: 2009-2013
Bioengineering Affiliate Faculty: Huzefa Rangwala (PI)
The recent development of various government and University funded screening centers has
provided the academic research community with access to state-of-the-art high-throughput and
high-content screening facilities. As a result, chemical genetics, which uses small organic
molecules to alter the function of proteins, has emerged as an important experimental technique
for studying and understanding complex biological systems. However, the methods used to
develop small-molecule modulators (chemical probes) of specific protein functions and analyze
the phenotypes induced by them have not kept pace with advances in the experimental screening
technologies. This project will develop novel algorithms in the areas of cheminformatics,
bioinformatics, and machine learning to analyze the publicly available information associated
with proteins and the molecules that modulate their functions (target-ligand activity matrix).
These algorithms will be used to develop new classes of computational methods and tools to aid
in the development of chemical probes and the analysis of the phenotypes elicited by small
molecules. The key hypothesis underlying this research is that the target-ligand activity matrix
contains a wealth of information that if properly analyzed can provide insights connecting the
structure of the chemical compounds (chemical space) to the structure of the proteins and their
functions (biological space).
CAREER: Annotating the Microbiome using Machine Learning Methods Funding Agency: NSF
Funding Agency: NSF
Budget: $550,000
Dates: 2013-2018
Bioengineering Affiliate Faculty: Huzefa Rangwala (PI)
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This project addresses an important challenge of developing sophisticated and novel machine
learning techniques for complex real-world problems. New technologies allow us to determine
the genomes of organisms co-existing within various ecosystems ranging from ocean, soil and
human-body. Several researchers have embarked on studying the pathogenic role played by the
microbiome, defined as the collection of microbial organisms within the human body, with
respect to human health and disease conditions. The research activities in this CAREER project
will develop approaches for the identification of taxonomy, function and metabolic potential
from the collective genomes samples. A key contribution will be the development of multi-task
learning approaches that combine information across multiple hierarchical databases associated
with the annotation problems. During research, the PI will investigate the best ways to capture
the underlying hierarchical structure, prevalent within different annotation databases. The
rationale underlying this proposed research is that there is a wealth of complementary
information that exists across several manually curated biological databases. Associating
microbiome with phenotype requires integration of various high-throughput omic data sources
(genomic, metabolic, proteomic) that may not be uniformly available across all samples.
A Unified Computational Framework to Enhance the Ab-initio Sampling of Native-like Protein
Conformations
Funding agency: NSF
Budget: $449,000
Dates: 2010-2013
Bioengineering Affiliate Faculty: Amarda Shehu (PI)
The research involves the design and analysis of a framework to compute the spatial
arrangements, also known as conformations, in which a protein chain of amino acids is
biologically-active (in its native state). This is an important goal towards understanding protein
function. While proteins are central to many biochemical processes, little is known about
millions of protein sequences obtained from organismal genomes. The intellectual merit of this
work lies in the development of a novel computational framework that combines probabilistic
exploration with the theory of statistical mechanics to efficiently enhance the sampling of the
conformational space near the native state. Low-dimensional projections guide the exploration
towards low-energy and geometrically-diverse conformations. Additional intellectual merit lies
in the incorporation of knowledge and observations emerging from biophysical theory and
experiment, such as the use of coarse graining, relation between energy barrier height and
temperature, and hierarchical organization of tertiary structure. Algorithmic components of the
framework will be systematically evaluated for efficiency, accuracy, and how they enhance the
sampling of the conformational space near the native state.
CAREER: Probabilistic Methods for Addressing Complexity and Constraints in Protein Systems
Funding agency: NSF
Budget: $549,000
Dates: 2012-2017
Bioengineering Affiliate Faculty: Amarda Shehu (PI)
The research addresses fundamental issues in protein modeling. Understanding proteins in silico
involves searching a vast high-dimensional conformational space of inherently flexible systems
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with numerous inter-related degrees of freedom, complex geometry, physical constraints, and
continuous motion. Three core research directions are identified. (1) Geometric constraints
underlying protein motion are not trivial to identify or address. The proposed research exploits
mechanistic analogies between proteins and robot kinematic linkages and investigates inverse
kinematics techniques to efficiently formulate and address complex geometric constraints arising
in diverse protein studies. (2) The funnel-like protein energy landscape exposes physics-based
energetic constraints that are often demanding to address in silico. The proposed research
pursues a multiscale treatment of energetic constraints in the context of probabilistic search,
supporting coarse- and fine-grained levels of protein representational detail and converting
between them with information gathered during exploration. (3) The conformational ensemble
view of the protein state relevant for function necessitates search algorithms capable of exploring
the high-dimensional conformational space and its rugged energy landscape. A novel
probabilistic search framework is proposed that gathers information about the space it explores
and employs this information to advance towards promising unexplored regions of the space.
Taken together, these research directions allow addressing complexity in proteins by formulating
and exploiting geometric and energetic constraints, thus narrowing the search space of interest to
regions where the constraints are satisfied, and by employing a novel probabilistic framework
with enhanced sampling capability able to feasibly search the relevant regions of the space.
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Letters of Support from Potential Employers
1. Dr. Joel Myklebust, Deputy Director, Office of Science and Engineering
Laboratories, Center for Devices and Radiological Health, FDA
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