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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10/29/2004 Bioinformatics in Computer Science 1 Bioinformatics in Computer Science, the Virginia Bioinformatics Institute, and Opportunities for Engineering.
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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. HeathDepartment of Computer Science
Blacksburg, VA 24061
College of EngineeringAdvisory Board Meeting
October 29, 2004
10/29/2004 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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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
• 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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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
10/29/2004 Bioinformatics in Computer Science 20
Alexey Onufriev
• Assistant Professor of Computer Science• Hired in 2003 for bioinformatics group• Expertise: Computational and theoretical biophysics and
• Projects:– Biomolecular electrostatics– Theory of cooperative ligand binding– Protein folding– Protein dynamics — how does myoglobin uptake oxygen?– Computational models of gene silencing
10/29/2004 Bioinformatics in Computer Science 21
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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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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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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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 Research in CS
• Collaboration• Funding• Resources• Overview of projects
10/29/2004 Bioinformatics in Computer Science 26
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.
10/29/2004 Bioinformatics in Computer Science 28
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 computingBioinformatics Research LAN• Linux, Mac OS X• Bioinformatics databases and analysis
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JigCell: A PSE for JigCell: A PSE for Eukaryotic Cell Cycle ControlsEukaryotic 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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Clb5MBF
P Sic1SCFSic1
Swi5
Clb2Mcm1
Unaligned chromosomes
Cln2Clb2
Clb5
Cdc20 Cdc20
Cdh1
Cdh1
Cdc20
APC
PPX
Mcm1
SBF
Esp1Esp1 Pds1
Pds1
Cdc20
Net1
Net1P
Cdc14
RENT
Cdc14
Cdc14
Cdc15
Tem1
Bub2
CDKs
Esp1
Mcm1 Mad2
Esp1
Unaligned chromosomes
Cdc15
Lte1
Budding
Cln2SBF
?
Cln3
Bck2and
growth
Sister chromatid separation
DNA synthesis
Cell Cycle of Budding Yeast
10/29/2004 Bioinformatics in Computer Science 32
JigCell Problem-Solving Environment
Experimental Database
Wiring Diagram
Differential Equations Parameter Values
Analysis Simulation
VisualizationAutomatic Parameter Estimation
10/29/2004 Bioinformatics in Computer Science 33
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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Expresso:A Next Generation Software
System for Microarray Experiment Management
and Data Analysis
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Scenarios for Effects of Abiotic Stress on Gene Expression in Plants
10/29/2004 Bioinformatics in Computer Science 36
The Expresso Pipeline
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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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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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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
10/29/2004 Bioinformatics in Computer Science 49
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
• 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