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Computational Biology and Bioinformatics in Computer Science
Lenwood S. HeathDepartment of Computer Science
2160J Torgersen HallVirginia Tech
Department Seminar SeriesSeptember 9, 2005
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Overview
• Computational biology and bioinformatics (CBB)•What is it?•History at VT•Some biological terminology
• CBB faculty and projects
• Education in CBB•Bioinformatics option•GBCB
• Conclusion
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Computational Biology and Bioinformatics (CBB)
• 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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CBB History (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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CBB History (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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• 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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CBB History (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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Some Molecular Biology
•The encoded instruction set for an organism is kept in DNA molecules.
• Each DNA molecule contains 100s or 1000s of genes.
•A gene is transcribed to an mRNA molecule.
• An mRNA molecule is translated to a protein (molecule).
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Elaborating Cellular Function
DNA mRNA ProteinTranscription Translation
ReverseTranscription
Degradation
Regulation
Protein functions:• Structure• Catalyze chemical reactions• Regulate transcription
(Genetic Code)
Thousands of Genes!
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Chromosomes
• Large molecules of DNA: 104 to 108 base pairs.• Human chromosomes: 22 matched pairs plus X and
Y.• A gene is a subsequence of a chromosome that
encodes a protein.• Proteins associated with regulation are present in
chromosomes.• Every gene is present in every cell.• Only a fraction of the genes are in use
(“expressed”) at any time.
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Genomics: Discovery of genetic sequences and the ordering of those sequences into individual genes, into gene families, and into chromosomes. Identification of sequences that code for gene products/proteins and sequences that act as regulatory elements.
Genomics
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Functional Genomics: The biological role of individual genes, mechanisms underlying the regulation of their expression, and regulatory interactions among them.
Functional Genomics
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Challenges for Computer Science
• Analyzing and synthesizing complex experimental data
• Representing and accessing vast quantities of information
• Pattern matching• Data mining• Gene discovery• Function discovery• Modeling the dynamics of cell function
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CBB Faculty in CS
1. Chris Barrett (VBI, CS)
2. Vicky Choi
3. Roger Ehrich
4. Edward A. Fox
5. Lenny Heath
6. Madhav Marathe (VBI, CS)
7. T. M. Murali
8. Chris North
9. Alexey Onufriev
10. Naren Ramakrishnan
11. Adrian Sandu
12. Eunice Santos
13. João Setubal (VBI, CS)
14. Cliff Shaffer
15. Anil Vullikanti (VBI, CS)
16. Layne Watson
17. Liqing Zhang
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Established CBB Faculty
• Layne Watson• Lenny Heath• Cliff Shaffer• Naren Ramakrishnan• Eunice Santos
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Layne Watson
• Professor of Computer Science and Mathematics• Expertise: algorithms; image processing; high
performance computing; optimization; 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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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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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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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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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 CBB 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, Madhav Marathe (Fall 2004) VBI and CS
• Anil Vullikanti (Fall 2004) VBI and CS
• Yang Cao (January, 2006) CS bioinformatics hire
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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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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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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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Selected CBB Research Projects
• JigCell
• Expresso
• Multimodal Networks
• Computational Modeling of Gene Silencing
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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
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JigCell Problem-Solving Environment
Experimental Database
Wiring Diagram
Differential Equations Parameter Values
Analysis Simulation
VisualizationAutomatic 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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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
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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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Fusion — Chris North
• “Snap together” visualization environment
• Interactively linked data from multiple sources
• Data mining in the background
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• Evolution implies changes in genomic sequence through mutations and other mechanisms
• Genomic or protein sequences that are similar are called homologous
• Algorithms to detect homology provide access to evolutionary relationships and perhaps function conservation through genomic data.
Sequence Analysis
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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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Computational Modeling of Computational Modeling of Gene Silencing (CMGS)Gene Silencing (CMGS)
Lenwood S. Heath, Richard Helm, Alexey Onufriev,
Naren Ramakrishnan, and Malcolm Potts
Departments of Computer Science and Biochemistry
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RNA Interference (RNAi)RNA Interference (RNAi)
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CMGS SystemCMGS System
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Other CBB Research Projects
• Bacterial genomics — Setubal• xMotif — Murali• Plant Orthologs and Paralogs (POPS)
– Heath, Murali, Setubal, Zhang, Ruth Grene (plant physiology)
• Protein structure and docking — Choi• Whole-genome functional annotation — Murali• Modeling biomolecular systems — Onufriev
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CBB Education at VT
• CS has been training CS graduate students in CBB 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 third year
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CBB Education in CS
• A key department of the Ph.D. program in Genetics, Bioinformatics, and Computational Biology (GBCB)
• Computation for the Life Sciences I, II• Algorithms in Bioinformatics• Systems Biology• Structural Bioinformatics and Computational
Biophysics• Databases for Bioinformatics
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Conclusions
• Important research area in department• Close collaboration between life scientists and
computational scientists from the beginning of CBB research at VT
• 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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Supported by:Next Generation Software
Information Technology Research
NSF