Differential display analysis of alteration in gene expression

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Differential display analysis of alteration in gene expression Powered By Docstoc
					 9th International Conference on
Intelligent System for Molecular

     Tivoli Gardens, Copenhagen, Denmark
                July 19-26, 2001

                Park, Ji-Yoon
   Satellite Meetings(July 19-20)

July 19
  - The Open Source author’s contract : Steven Brenner
  - BioJava project report: Thomas Down and Matthew Pocock
  - Biopython project report: Andrew Dalke
  - BioCORBA project report: Jason Stajich
  - biok: Catherine Letondal
  - Lightning Talks:
      • OMG’s new Model Driven Architecture(MDA): Scott Markel
      • BioRuby: Yoshinori Okuju, Toshiaki Katayama, and Mitsuteru Nakao
      • Genquire: David Block
      • Generation and use of substitution matrices in Biopython: IDDo Friedberg
         and Brad Chapman
   Satellite Meetings(July 19-20)

July 20
  : Biopathway
  - Bioperl project report: Hilmar Lapp
  - EnsEMBL project report: Arne Stabenau
  - Lightning Talks:
      • Bioperl-project: Ewan Birney
      • OpenBSA: Juha Muilu, Martin Senger and Alan Robinson
      • Genetic algorithm and neural network libraries: Brad Chapman
       • Mining gene expression information using a controlled hierarchical vocabulary
          : Peter van heusden
       • TFBS: Perl modules for transcription factor detection and analysis: Boris Lenhard
  - A tool suite for the Gene Ontology: Chris Mungall, Hohn Richter, Bradley Marshall, and
    Suzanna Lewis
  - DeCAL: A system for constructing comparative maps: Debra Goldberg, Jon Kleinberg, and
    Susan McCouch
   Tutorial( July 21)

* Morning Turorials: Sat July 21 8:30-12:30
   [Statistical analysis of micro-arrays studies]
     : Emmanuel Lazaridis, University of South Florida

Afternoon Turorials: Sat July 21 14:00-18:00
   [Network genomics]
     : Christian Forst, Los Alamos National Laboratory
  Sequence motifs, alignments and families:
 July 22
[Keynote: Protein folding, molecular evolution, and human disease]
  : Christopher M. Dobson, University of Oxford
     ►Protein misfolding in disease
     ►misfolded polypeptide           →            folded protein
                ↓                  misfolding           ↓
          improper trafficking    toxic folding    degradation

     ►Asp67His: Amyloid formation(; aggregation)
     ►SH3 domain of   PI3 kinase: Cross- structure
 Sequence motifs, alignments and families:
July 22
* An insight into domain combinations
* Prediction of the coupling specificity of G protein coupled
   receptors to their G proteins
* Improved prediction of the number of residue contacts in
   proteins by recurrent neural networks
* Non-symmetric score matrices and the detection of homologous
   transmembrane proteins
* Generating protein interaction maps from incomplete data:
   application to fold assignment
Sequence motifs, alignments and families:
July 22
[Keynote - Structural Genomics]
   - Christopher M. Dobson, University of Oxford
   - Goal : All protein domains carry all functional families
          How many experimental structure?
  - Coordination of international programs in structural genomics
  - Pathways in expression profile

* 0j-py: a software tool for low complexity proteins and protein domains
 : Michael J. Wise, Centre for Communications Systems Research
    → new tool for looking peptide

* Separating real motifs from their artifacts
* Feature selection for DNA methylation based cancer classification
* An algorithm for finding signals of unknown length in DNA sequences
   Networks and Modeling: July 23

[Keynote- Protein Interactions]
 : David Eisenberg, University of California, LosAngeles
   • Rossetta Stone
      - Fusion of functionally-linked domain
      - http:// dip.doe-mbi.ucla.edu
   • Phylogenic profile
      - correlated occurrence of pairs of proteins in genomes
   • Gene function
   • Database interacting protein
   • 3D domain swapping
   • Signaling path
  Networks and Modeling: July 23

<Protein-protein interaction map inference using interacting domain
  profile pairs>
   : Jerome Wojcik, Vincent Schachter, Hybrigenics S.A
     ► Computational prediction of protein network
     ► IDPP(Interacting domain profile pair) method
        • Interacting domain cluster = vertics
        • Interacting domain profile pair = edge
     ►Assessment of predictive accuracy: reference data problem

* Inferring subnetworks from perturbed expression profiles
   : http:// www.cs.huji.ac.il/labs/combio
* Molecular classification of multiple tumor types
   : http:// geone.wi.mit.edu/MPR
* Centralization: a new method for the normalization of gene expression data
   Networks and Modeling: July 23
[ Centralization: a new method for the normalization of gene expression data ]
   * Housekeeping approach is questionable.
   * Basic assumption
      ; roughly the gene level expression no preffered direct of regulation
         - real data: differently & strongly regulation
         - total RNA vary: different cell state/tissue
         - different factors are summed
         - only subset of all gene measured
         - strongly expressed gene be regulated: main protein product
   * Advantage
         - more robus on real data
         - inexpensive alternative experiment
         - Easy
 Keynote- The phenomenon of the web: David Eisenberg, University of
    California, Los Angeles
   Networks and Modeling: July 23
[Keynote- The phenomenon of the web]
  : David Eisenberg, University of California, Los Angeles
     http: // rana.lbl.gov
     http:// genome-www.standford. edu./microarray
 Gene structure, Regulation, and
 Modeling: July 24
* Keynote- The Modern RNA world: many genes don’t encode proteins
          : Sean Eddy, Washington University
* Promoter prediction in the human genome
* Joint modeling of DNA sequence and physical properties to improve
   eukaryotic promoter recognition
* Computational expansion of genetic networks
* GENIES: a natural-language processing systems for the extraction of
   molecular pathways from journal articles
* Designing better phages
* Computational Analysis of RNA splicing
* Disambiguating proteins, genes, and RNA in text: a machine learning
* Gene recognition based on DAG shortest paths
* An efficient algorithm for finding short approximate non-tandem
   Methods: July 25
* Keynote- Membrane proteins: From the computer to the bench and
   back: Gunnar von Heijne, Stockholm University
* Design of a compartmentalized shotgun assembler for the human
* Probabilistic approaches to the use of higher order clone
   relationships in physical map assembly:
* Fragment assembly with double-barreled data
* SCOPE: a probabilistic model for scoring tandem mass spectra
   against a peptide database
* Probe selection algorithms with applications in the analysis of
   microbial communities
* Fast optimal leaf ordering for hierarchical clustering
* Separation of samples into their constituents using gene expression
  WEB: July 26
* Education in Bioinformatics: Current Trends and Issues.
   - Shoba Ranganathan
* Opening Addresss: Bioinformatics Education
   - Looking to the Future: Russ Altman
* The S* Life Science Informatics Alliance
   - Shoba Ranganathan
* Bioinformatics BS at the Univerisity of California, Santa Cruz
   - Kevin Karplus
* A Masters Degree in Bioinformatics in Switzerland
   - Patricia Palagi
* Emerging US & UK Standards for Graduate Bioinformatics training
   - Linda Ellis
   WEB: July 26
* Bioinformatics Course Delivery: Tools and Infrastructure. Siv
* Bioinformatics: Introducing the concept of “ evaluation-based”
   learning : Siv Andersson
* Problem-oriented sequence analysis tool: Ueng-Cheng Yang
* EMBER- A European Multimedia Bioinformatics Educational
   Resource: C. Victor Jongeneel
* Virtual Reality and Visualization for Bioinformatics Education: YY
* Starting a new Bioinformatics Program. Phyllis Gardner(Chair)
* Initiating a multi-disciplinary, trans-institutional program: A Dean’s
   perspective: Phyllis Gardner
* Insights into starting a new Multi-disciplinary program: Betty
   WEB: July 26
* Bioinformatics Training. Frederique Galisson(Chair)
* The Canadian Bioinformatics Workshops: Stephen Herst
* The BioNavigator Education Package- resources for practical
   instruction in bioinformatics: Bruno A. Gaeta
* The Human Genome Mapping Project Resources Centre-
   Encouraging Bioinformatics Awareness: Lisa Mullan
* Panel Discussion: Betty Cheng(Chair)
  The S* Life Science Informatics Alliance: Question Time: S* Team
* Concluding Session: Closing Remarks. Shoba Ranganathan(Chair)
  Bioinformatics Education: Future trends and perspectives: Philip
Free Energy( ∆ G )

Thermodynamic constant that gives the amount of energy required
 for or released by a reaction
  - kcal/mol
  - Reaction that require energy ; positive
  - Reation that release free energy ; negative
  - Energy must be released overall to form a base-paired structure
  - The stability of the structure is determined by the amount of
 energy released
Hairpin Structure
The Overall Free Energy of a double-
stranded structure
∆ G total = ∆ G i + ∑ ∆ Gx + ∆ ∑ Gu

∆ Gi : the free energy for initiation of a double helix
           Positive value: + 3.4 kcal/mol
           It applied to intermolecular duplex formation

∑ ∆ Gx: the sum of the individual reactions involved in propagating the
        double helix as each base pair is added
         the formation of each base pair releases energy ; negative

∑∆Gu: the sum of individual instances encountered as the double helix is
       propagated in which the opposing bases are not complementary
        the energy required to hold these bases in an unpaired state
           ; positive
The Free Energy of formation for a
   potential base-paired region
Free Energy of Base Pairing

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