10/29/2004 Bioinformatics in Computer Science - People - Virginia ...

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10/29/2004


Bioinformatics in Computer Science

1

Bioinformatics in Computer Science,
the Virginia Bioinformatics Institute,
and Opportunities for Engineering

Lenwood S. Heath

Department of Computer Science

Blacksburg, VA 24061

College of Engineering

Advisory Board Meeting

October 29, 2004

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Bioinformatics in Computer Science

2

Overview


Computational biology and bioinformatics


The players



Computer Science



Virginia Bioinformatics Institute (VBI)



Others at VT


Opportunities for the College



Collaboration with VBI



SBES, Wake Forest School of Medicine



NIH and DHS funding



Scientific modeling

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Bioinformatics in Computer Science

3

Computational Biology and
Bioinformatics


Computational biology


computational research inspired by
biology


Bioinformatics


application of computational research
(computer science, mathematics, statistics) to advance basic and
applied research in the life sciences



Agriculture



Basic biological science



Medicine


Both ideally done within
multidisciplinary collaborations

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Bioinformatics in Computer Science

4

Bioinformatics at VT (Part I)


Biological modeling (Tyson, Watson):

> 20 years


Computational biology, genome rearrangements
(Heath): > 10 years



Fralin Biotechnology sponsored faculty advisory
committee centered on bioinformatics: 1998
-
2000


Biochemistry; biology; CALS; computer science
(Heath, Watson); statistics; VetMed


Provost provided $1 million seed money


First VT bioinformatics hire (Gibas, biology, 1999)

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Bioinformatics in Computer Science

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Bioinformatics at VT (Part II)


Outside initiative submitted to VT for a campus
bioinformatics center


1998


Discussions of bioinformatics advisory committee
contributed to a proposal to the Gilmore
administration


1999


Governor Gilmore puts plans and money for
bioinformatics center in budget


1999
-
2000


Virginia Bioinformatics Institute (VBI) established
July, 2000; housed in CRC

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Bioinformatics in Computer Science

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Bioinformatics at VT (Part III)


Bioinformatics course and curriculum development began
with faculty subcommittee


1999


Courses supporting bioinformatics now in many life
science and computational science departments, including:


Biology


Biochemistry


Computer Science


Plant Pathology, Physiology, and Weed Science (PPWS)


Mathematics


Statistics

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Bioinformatics in Computer Science

7

Bioinformatics Education at VT


CS has been training CS graduate students in
bioinformatics since 2000


Graduate bioinformatics option established in a
number of participating departments


2003


Ph.D. program in Genetics, Bioinformatics, and
Computational Biology (GBCB)


2003


First GBCB students arrived, Fall, 2003; now in
second year; completing core requirements

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Bioinformatics in Computer Science

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Bioinformatics Spirit at VT


Close collaboration between life scientists and
computational scientists from the beginning


Educational approach insists on adequate
multidisciplinary background


Multidisciplinary collaborators work closely on a
regular basis


Contributions to biology or medicine essential
outcomes

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Bioinformatics in Computer Science

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The Players


Computer Science



Virginia Bioinformatics Institute (VBI)



Others at VT

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Bioinformatics in Computer Science

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CS Bioinformatics Faculty

1.
Chris Barrett (VBI, CS)

2.
Vicky Choi

3.
Roger Ehrich

4.
Edward A. Fox

5.
Lenny Heath

6.
T. M. Murali

7.
Chris North

8.
Alexey Onufriev

9.
Naren Ramakrishnan


10.
Adrian Sandu

11.
Eunice Santos

12.
João Setubal (VBI, CS)

13.
Cliff Shaffer

14.
Layne Watson

15.
Liqing Zhang

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Relevant Expertise


Algorithms



Choi, Heath, Santos, Setubal, Shaffer, Watson


Computational structural biology


Onufriev, Sandu


Computational systems biology


Murali


Data mining


Ramakrishnan


Genomics


Heath, Murali, Ramakrishnan, Setubal, Zhang


Human
-
computer interaction, visualization


North


Image processing


Ehrich, Watson


Information retrieval



Ehrich


High performance computing



Sandu, Santos, Watson


Optimization


Watson


Simulation


Barrett

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Bioinformatics in Computer Science

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Established Bioinformatics Faculty


Layne Watson


Lenny Heath


Cliff Shaffer


Naren Ramakrishnan


Eunice Santos

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Bioinformatics in Computer Science

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Layne Watson


Professor of Computer Science and Mathematics


Expertise:
algorithms; i
mage processing; h
igh
performance computing; o
ptimization; scientific
computing


Computational biology:
has worked with John Tyson
(biology) for over 20 years


JigCell:
cell
-
cycle modeling environment; with Tyson,
Shaffer, Ramakrishnan, Pedro Mendes of VBI


Expresso:
microarray experimentation; with Heath,
Ramakrishnan

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Bioinformatics in Computer Science

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Lenny Heath


Professor of Computer Science


Expertise:
algorithms; theoretical computer science;
graph theory


Computational biology:
worked in genome
rearrangements 10 years ago


Bioinformatics:
concentration in past 5 years


Expresso:
microarray experimentation; with
Ramakrishnan, Watson


Multimodal networks


Computational models of gene silencing

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Bioinformatics in Computer Science

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Cliff Shaffer


Associate Professor of Computer Science


Expertise:
algorithms; problem solving
environments; spatial data structures;


JigCell:
cell
-
cycle modeling environment; with
Ramakrishnan, Tyson, Watson

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Bioinformatics in Computer Science

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Naren Ramakrishnan


Associate Professor of Computer Science


Expertise:
data mining; machine learning; problem
solving environments


JigCell:
cell
-
cycle modeling problem solving
environment; with Shaffer, Watson


Expresso:
microarray experimentation; with Heath,
Watson


Proteus


inductive logic programming system for
biological applications


Computational models of gene silencing

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Bioinformatics in Computer Science

17

Eunice Santos


Associate Professor of Computer Science


Expertise:
Algorithms;

computational biology;

computational complexity; parallel and
distributed processing; scientific computing


Relevant bioinformatics project:
modeling
progress of breast cancer

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New Bioinformatics Faculty


T. M. Murali
(2003) CS bioinformatics hire


Alexey Onufriev
(2003) CS bioinformatics hire


Adrian Sandu

(2004) CS hire


João Setubal
(Early 2004) VBI and CS


Vicky Choi
(2004) CS bioinformatics hire


Liqing Zhang
(2004) CS bioinformatics hire


Chris Barrett
(Fall 2004) VBI and CS


One more bioinformatics position for Fall, 2005


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Bioinformatics in Computer Science

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T. M. Murali


Assistant Professor of Computer Science


Hired in 2003 for bioinformatics group


Expertise:
algorithms; computational geometry;
computational systems biology


Projects:


Functional gene annotation


xMotif


find patterns of coexpression among subsets of
genes


RankGene


rank genes according to predictive power for
disease

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Bioinformatics in Computer Science

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Alexey Onufriev


Assistant Professor of Computer Science


Hired in 2003 for bioinformatics group


Expertise:
Computational and theoretical biophysics and
chemistry; structural bioinformatics; numerical
methods; scientific programming


Projects:


Biomolecular electrostatics


Theory of cooperative ligand binding


Protein folding


Protein dynamics


how does myoglobin uptake oxygen?


Computational models of gene silencing


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Bioinformatics in Computer Science

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Adrian Sandu


Associate Professor of Computer Science


Hired in 2003


Expertise:
Computational science; numerical methods;
parallel computing; scientific and engineering
applications


Computational science:


New generation of air quality models


computational tools for assimilation of atmospheric chemical
and optical measurements into atmospheric chemical
transport models

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Bioinformatics in Computer Science

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João Setubal


Research Associate Professor

at VBI


Associate Professor

of Computer Science


Joined in early 2004


Expertise:
algorithms; computational biology;
bacterial genomes


Comparative genomics

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Bioinformatics in Computer Science

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Vicky Choi


Assistant Professor of Computer Science


Hired in 2004 for bioinformatics group


Expertise:
computational biology; algorithms


Projects:


Algorithms for genome assembly


Protein docking


Biological pathways

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Bioinformatics in Computer Science

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Liqing Zhang


Assistant Professor of Computer Science


Hired in 2004 for bioinformatics group


Expertise:
evolutionary biology; bioinformatics


Research interests:


Comparative evolutionary genomics


Functional genomics


Multi
-
scale models of bacterial evolution

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Bioinformatics in Computer Science

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Bioinformatics Research in CS


Collaboration


Funding


Resources


Overview of projects

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Selected Collaborations


Virginia Tech:
Biochemistry, Biology,
Fralin Biotechnology Center, PPWS,
Veterinary Medicine, VBI, Wood Science


North Carolina State University:
Forest
Biotechnology Center


Duke:
Biology


University of Illinois:
Plant Biology

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Selected Funding (Watson/Tyson)


NSF MCB
-
0083315:
Biocomplexity
---
Incubation Activity: A
Collaborative Problem Solving Environment for
Computational Modeling of Eukaryotic Cell Cycle Controls.
J. J. Tyson,

L. T. Watson, N. Ramakrishnan, C. A. Shaffer,

J. C. Sible.

$99,965.


NIH 1 R01 GM64339
-
01:

``Problem Solving Environment
for Modeling the Cell Cycle.
J. J. Tyson, J. Sible, K. Chen,

L.
T. Watson, C. A. Shaffer, N. Ramakrishnan,

P. Mendes
(VBI).
$211,038.


Air Force Research Laboratory F30602
-
01
-
2
-
0572:

The
Eukaryotic Cell Cycle as a Test Case for Modeling Cellular
Regulation in a Collaborative Problem Solving
Environment.
J. J. Tyson, J. C. Sible, K. C. Chen,

L. T.
Watson, C. A. Shaffer, N. Ramakrishnan.

$1,650,000.

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Selected Funding (Heath, et al.)


NSF IBN 0219322:

ITR: Understanding Stress Resistance
Mechanisms in Plants: Multimodal Models Integrating
Experimental Data, Databases, and the Literature.
L. S.

Heath;

R. Grene, B. I. Chevone,

N. Ramakrishnan,

L. T. Watson.

$499,973.


NSF

EIA
-
01903660:

A Microarray Experiment Management
System.
N. Ramakrishnan, L. S. Heath, L. T. Watson,

R. Grene,

J. W. Weller (VBI).
$600,000.


DARPA

N00014
-
01
-
1
-
0852:

Dryophile Genes to Engineer Stasis
-
Recovery of Human Cells.
M. Potts,

L. S. Heath,

R. F. Helm,
N.
Ramakrishnan,

T. O. Sitz, F. Bloom, P. Price (Life
Technologies), J. Battista (LSU).
$4,532,622.


NSF CCF 0428344:

ITR
-
(NHS)
-
(sim): Computational Models
for Gene Silencing: Elucidating a Pervasive Biological Defensive
Response.
L. S.

Heath,

R. F. Helm,

A. Onufriev,
M. Potts,

N.
Ramakrishnan.

$1,500,000.

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Research Resources Available
to CS Bioinformatics

System X


Third fastest computer on the planet (2003)

Laboratory for Advanced Scientific Computing &
Applications (LASCA)


Parallel algorithms & math software


Anantham Cluster


Grid computing

Bioinformatics Research LAN


Linux, Mac OS X


Bioinformatics databases and analysis


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Bioinformatics in Computer Science

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JigCell: A PSE for

Eukaryotic Cell Cycle Controls

Marc Vass, Nick Allen, Jason Zwolak, Dan Moisa,

Clifford A. Shaffer, Layne T. Watson,

Naren Ramakrishnan, and
John J. Tyson

Departments of Computer Science and
Biology

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Clb5

MBF

P

Sic1

SCF

Sic1

Swi5

Clb2

Mcm1

Unaligned
chromosomes


Cln2

Clb2

Clb5

Cdc20

Cdc20

Cdh1

Cdh1

Cdc20

APC

PPX

Mcm1

SBF

Esp1

Esp1

Pds1

Pds1

Cdc20

Net1

Net1P

Cdc14

RENT

Cdc14

Cdc14

Cdc15

Tem1

Bub2

CDKs

Esp1

Mcm1

Mad2

Esp1

Unaligned
chromosomes


Cdc15

Lte1

Budding

Cln2

SBF

?

Cln3

Bck2

and

growth

Sister chromatid
separation

DNA synthesis

Cell Cycle of Budding Yeast

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JigCell Problem
-
Solving Environment

Experimental Database

Wiring

Diagram

Differential

Equations

Parameter

Values

Analysis

Simulation

Visualization

Automatic Parameter Estimation

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Why do these calculations?


Is the model “yeast
-
shaped”?


Bioinformatics role: the model organizes
experimental information.


New science: prediction, insight



JigCell is part of the DARPA BioSPICE suite of
software tools for computational cell biology.

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Bioinformatics in Computer Science

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Expresso:

A Next Generation Software
System for Microarray
Experiment Management
and Data Analysis

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Bioinformatics in Computer Science

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Scenarios for Effects of Abiotic Stress
on Gene Expression in Plants

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Bioinformatics in Computer Science

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The Expresso Pipeline

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Bioinformatics in Computer Science

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Proteus


Data Mining with ILP


ILP (inductive logic programming)


a data mining
algorithm for inferring relationships or
rules


Proteus


efficient system for ILP in bioinformatics
context


Flexibly incorporates
a priori

biological knowledge (e.g.,
gene function) and experimental data (e.g., gene
expression)


Infers rules
without explicit direction



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Bioinformatics in Computer Science

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Networks in Bioinformatics




Mathematical Model(s) for Biological Networks



Representation: What biological entities and parameters to
represent and at what level of granularity?



Operations and Computations: What manipulations and
transformations are supported?



Presentation: How can biologists visualize and explore
networks?

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Reconciling Networks

Munnik and Meijer,

FEBS Letters,

2001

Shinozaki and Yamaguchi
-
Shinozaki,
Current Opinion
in Plant Biology,
2000

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Multimodal Networks



Nodes and edges have flexible semantics to represent:

-

Time

-

Uncertainty

-

Cellular decision making; process regulation

-

Cell topology and compartmentalization

-

Rate constants

-

Phylogeny



Hierarchical

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Using Multimodal Networks



Help biologists find
new biological knowledge



Visualize and explore



Generating
hypotheses

and experiments



Predict

regulatory phenomena



Predict

responses to stress



Incorporate into Expresso as part of
closing the loop

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Bioinformatics in Computer Science

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Fusion


Chris North


“Snap together”
visualization
environment


Interactively
linked data from
multiple sources


Data mining in
the background

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Established by the state in July, 2000; high visibility


Applies computational and information technology in
biological research


Research faculty (currently, about 18) expertise includes


Biochemistry


Comparative Genomics


Computer Science


Drug Discovery


Human and Plant Pathogens


More than $43 million funded research

Virginia Bioinformatics Institute (VBI)


Mathematics


Physics


Simulation


Statistics

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At The Virginia Bioinformatics Institute, we research
biological systems and design, develop and disseminate
technologies to make discoveries that
improve the
quality of human life.


We focus on understanding biology through systems that
integrate the interaction between organisms and their
environment

for the benefit of science and society.


We also strive to collaborate with the scientific community
by enabling transformation of information into useful
knowledge and by providing scientific services.


VBI Mission Statement

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The Disease Triangle

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Core lab facilities


DNA sequencing


Gene expression


Proteomics


Metabolomics


Core computational facilities


Cluster computing dedicated to bioinformatics


Data storage


Visualization


Database administration


Specialized VBI Facilities

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Originally housed in Corporate Research Center


Partially moved to campus last year


Bioinformatics I building


Final move to campus, December, 2004


Bioinformatics II
building


Total space in Bioinformatics I and II will be 130,560 square feet

VBI Integration into Main Campus

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VBI Research Portfolio ( by sponsor )

38%

25%

12%

12%

5%

5%

1%

1%

1%

National Institutes of Health

National Science Foundation

VT (JHU/ASPIRES/VTF)

U.S. Dept of Defense

CTRF

Other Academic Institutions

Industry

U.S. Dept of Agriculture

Foundations

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Funded Partnerships with VT Departments


Aerospace and Ocean Engineering


Biochemistry


Biology


Biomedical Science and Pathobiology, VMRCVM


Computer Science


Crop and Soil Environmental Sciences


Electrical and Computer Engineering


Fisheries and Wildlife Science


Horticulture


Mathematics


Plant Pathology, Physiology, and Weed Science


Statistics

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Opportunities for CS and the
College of Engineering


Collaboration with VBI



SBES, Wake Forest School of Medicine



NIH and DHS funding



Scientific modeling

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Collaboration with VBI


Basic biological science


molecular biology,
functional genomics, systems biology


Computational methods to answer biological
questions from vast stores of VBI data resources


Computational models and simulation of
biological systems, e.g., host
-
pathogen
interaction

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SBES, Wake Forest


Medical research

includes significant computational challenges


Much analysis can be done without additional lab biology


Biomedical data analysis and mining


Identification of genes responsible for complex traits


More flexible and useful medical instrumentation


Precise identification of disease


Treatment suggestion


Prognosis prediction

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NIH and DHS Funding


Bioinformatics is one of the New Pathways to
Discovery in the NIH Roadmap


Computation is essential to advancing medical
practice, from diagnosis to drug design



Department of Homeland Security (DHS) is funding
research to respond to bioterrorism


Detection and identification of agents


Rapid response to threats


Modeling crisis impact and response

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Scientific Modeling


Protein folding


Protein function


Protein
-
protein interaction


Cellular signaling and decision processes


Heart, lung, neurological function


System X is an essential component

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Conclusion


Bioinformatics is an emerging area of
opportunity, but challenging to enter


Rapid developments the norm; flexibility
essential


Virginia Tech and the College are well
-
positioned to take advantage