1. Computational Biology and Bioinformatics in Computer Science
Lenwood S. Heath
Department of Computer Science
2160J Torgersen Hall
Virginia Tech
Department Seminar Series
September 9, 2005

2. 9/9//2005 Computational Biology and Bioinformatics
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
9/9// Computational Biology and Bioinformatics

3. 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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4. 9/9//2005 Computational Biology and Bioinformatics
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:
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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5. 9/9//2005 Computational Biology and Bioinformatics
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 —
Virginia Bioinformatics Institute (VBI) established July, 2000; housed in CRC
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6. 9/9//2005 Computational Biology and Bioinformatics
Virginia Bioinformatics Institute (VBI)
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
Mathematics
Physics
Simulation
Statistics
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7. 9/9//2005 Computational Biology and Bioinformatics
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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8. 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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9. Elaborating Cellular Function
Regulation
Degradation
Transcription
Translation
DNA
mRNA
Protein
(Genetic Code)
Reverse
Transcription
Protein functions:
Structure
Catalyze chemical reactions
Regulate transcription
Thousands of Genes!
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10. 9/9//2005 Computational Biology and Bioinformatics
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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11. 9/9//2005 Computational Biology and Bioinformatics
Genomics
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.
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12. 9/9//2005 Computational Biology and Bioinformatics
Functional Genomics
Functional Genomics: The biological role of individual genes, mechanisms underlying the regulation of their expression, and regulatory interactions among them.
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13. 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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14. 9/9//2005 Computational Biology and Bioinformatics
CBB Faculty in CS
Chris Barrett (VBI, CS)
Vicky Choi
Roger Ehrich
Edward A. Fox
Lenny Heath
Madhav Marathe (VBI, CS)
T. M. Murali
Chris North
Alexey Onufriev
Naren Ramakrishnan
Adrian Sandu
Eunice Santos
João Setubal (VBI, CS)
Cliff Shaffer
Anil Vullikanti (VBI, CS)
Layne Watson
Liqing Zhang
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15. Established CBB Faculty
Layne Watson
Lenny Heath
Cliff Shaffer
Naren Ramakrishnan
Eunice Santos
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16. 9/9//2005 Computational Biology and Bioinformatics
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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17. 9/9//2005 Computational Biology and Bioinformatics
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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18. 9/9//2005 Computational Biology and Bioinformatics
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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19. 9/9//2005 Computational Biology and Bioinformatics
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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20. 9/9//2005 Computational Biology and Bioinformatics
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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21. 9/9//2005 Computational Biology and Bioinformatics
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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22. 9/9//2005 Computational Biology and Bioinformatics
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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23. 9/9//2005 Computational Biology and Bioinformatics
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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24. 9/9//2005 Computational Biology and Bioinformatics
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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25. 9/9//2005 Computational Biology and Bioinformatics
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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26. 9/9//2005 Computational Biology and Bioinformatics
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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27. 9/9//2005 Computational Biology and Bioinformatics
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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28. Selected CBB Research Projects
JigCell
Expresso
Multimodal Networks
Computational Modeling of Gene Silencing
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29. 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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30. Cell Cycle of Budding Yeast
Sister chromatid separation
Cdc20
PPX
Lte1
Esp1
Budding
Pds1
Esp1
Tem1
Net1P
Esp1
Bub2
Cdc15
Cln2
SBF
Unaligned chromosomes
Pds1
SBF
Net1
RENT
Mcm1
Unaligned chromosomes
Cdh1
Mcm1
Cdc20
Mad2
Cdc20
Cdc14
Cln2
Clb2
Clb5
Cln3
Cdc15
and
Bck2
Cdh1
Mcm1
APC
Clb2
Cdc14
growth
Swi5
CDKs
SCF
Sic1 P
Sic1 ?
Cdc14
Cdc20
MBF
Clb5
DNA synthesis
Esp1
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31. JigCell Problem-Solving Environment
Experimental Database
Wiring Diagram
Differential Equations
Parameter Values
Analysis
Simulation
Visualization
Automatic Parameter Estimation
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32. 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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33. 9/9//2005 Computational Biology and Bioinformatics
Expresso:
A Next Generation Software System for Microarray Experiment Management and Data Analysis
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34. 9/9//2005 Computational Biology and Bioinformatics
Scenarios for Effects of Abiotic Stress on Gene Expression in Plants
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35. 9/9//2005 Computational Biology and Bioinformatics
The Expresso Pipeline
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36. 9/9//2005 Computational Biology and Bioinformatics
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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37. 9/9//2005 Computational Biology and Bioinformatics
Fusion — Chris North
“Snap together” visualization environment
Interactively linked data from multiple sources
Data mining in the background
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38. 9/9//2005 Computational Biology and Bioinformatics
Sequence Analysis
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.
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39. 9/9//2005 Computational Biology and Bioinformatics
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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40. 9/9//2005 Computational Biology and Bioinformatics
Reconciling Networks
Munnik and Meijer,
FEBS Letters, 2001
Shinozaki and Yamaguchi-Shinozaki, Current Opinion in Plant Biology, 2000
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41. 9/9//2005 Computational Biology and Bioinformatics
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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42. 9/9//2005 Computational Biology and Bioinformatics
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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43. Computational Modeling of 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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44. RNA Interference (RNAi)
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45. 9/9//2005 Computational Biology and Bioinformatics
CMGS System
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46. 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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47. 9/9//2005 Computational Biology and Bioinformatics
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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48. 9/9//2005 Computational Biology and Bioinformatics
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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49. 9/9//2005 Computational Biology and Bioinformatics
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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50. 9/9//2005 Computational Biology and Bioinformatics
Supported by: Next Generation Software Information Technology Research NSF
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