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Anil Vullikanti (VBI, CS) Layne Watson. Liqing Zhang ... Anil Vullikanti (Fall 2004) VBI and CS. Yang Cao (January, 2006) CS bioinformatics hire ... – PowerPoint PPT presentation

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


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
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

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

4
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)

5
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

6
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

7
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

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).

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!
10
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.

11
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.
12
Functional Genomics
Functional Genomics The biological role of
individual genes, mechanisms underlying the
regulation of their expression, and regulatory
interactions among them.
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

14
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

15
Established CBB Faculty
  • Layne Watson
  • Lenny Heath
  • Cliff Shaffer
  • Naren Ramakrishnan
  • Eunice Santos

16
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

17
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

18
Cliff Shaffer
  • Associate Professor of Computer Science
  • Expertise algorithms problem solving
    environments spatial data structures
  • JigCell cell-cycle modeling environment with
    Ramakrishnan, Tyson, Watson

19
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

20
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

21
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

22
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

23
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

24
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

25
João Setubal
  • Research Associate Professor at VBI
  • Associate Professor of Computer Science
  • Joined in early 2004
  • Expertise algorithms computational biology
    bacterial genomes
  • Comparative genomics

26
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

27
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

28
Selected CBB Research Projects
  • JigCell
  • Expresso
  • Multimodal Networks
  • Computational Modeling of Gene Silencing

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
30
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
31
JigCell Problem-Solving Environment
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.

33
Expresso A Next Generation Software System for
Microarray Experiment Management and Data Analysis
34
Scenarios for Effects of Abiotic Stress on Gene
Expression in Plants
35
The Expresso Pipeline
36
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

37
Fusion Chris North
  • Snap together visualization environment
  • Interactively linked data from multiple sources
  • Data mining in the background

38
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.

39
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?

40
Reconciling Networks
Munnik and Meijer, FEBS Letters, 2001
Shinozaki and Yamaguchi-Shinozaki, Current
Opinion in Plant Biology, 2000
41
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

42
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

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
44
RNA Interference (RNAi)
45
CMGS System
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

47
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

48
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

49
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

50
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