Computational%20Biology%20and%20Bioinformatics%20in%20Computer%20Science

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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
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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) gt 20 years
• Computational biology, genome rearrangements
  (Heath) gt 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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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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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
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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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
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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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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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

1. Naren Ramakrishnan
2. Adrian Sandu
3. Eunice Santos
4. João Setubal (VBI, CS)
5. Cliff Shaffer
6. Anil Vullikanti (VBI, CS)
7. Layne Watson
8. 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 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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Cell Cycle of Budding Yeast
Cdc20
Sister chromatid separation
PPX
Lte1
Esp1
Budding
Pds1
Tem1
Esp1
Net1P
Esp1
Bub2
Cdc15
Cln2
SBF
Unaligned chromosomes
Pds1
SBF
Net1
RENT
Mcm1
Unaligned chromosomes
Cdh1
Mcm1
Cdc20
Mad2
Cdc20
Cdc14
Cln3
Cdc15
and
Bck2
Cdh1
Mcm1
APC
Clb2
Cdc14
growth
CDKs
Swi5
SCF
Cdc14
?
Cdc20
MBF
Clb5
Esp1
DNA synthesis
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JigCell Problem-Solving Environment
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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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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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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 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)
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CMGS 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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