Transcript August 19, 2002 - People

Bioinformatics at Virginia Tech
David Bevan (BCHM)
Lenwood S. Heath (CS)
Ruth Grene (PPWS)
Layne Watson (CS)
Chris North (CS)
Naren Ramakrishnan (CS)
August 19, 2002
August 19, 2002
Slide 1
Overview
• Some relevant biology
• New language of biology
• Bioinformatics research at Virginia Tech
• Getting into bioinformatics at Virginia Tech
August 19, 2002
Slide 2
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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Slide 3
Transcription and Translation
Transcription
DNA
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Translation
mRNA
Protein
Slide 4
DNA Strand
A= adenine complements T= thymine
C = cytosine complements G=guanine
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Slide 5
RNA Strand
U=uracil replaces T= thymine
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Slide 6
Amino Acids
• Protein is a large molecule that is a chain of
amino acids (100 to 5000).
• There are 20 common amino acids
(Alanine, Cysteine, …, Tyrosine)
• Three bases --- a codon --- suffice to encode
an amino acid, according to the genetic code.
• There are also START and STOP codons.
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Slide 7
Translation to a Protein
Unlike DNA, proteins have three-dimensional structure
Protein folds to a three-dimensional shape that
minimizes energy
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Slide 8
The Language of the New
Biology
A new language has been created. Words in the
language that are useful today.
Genomics
Functional Genomics
Proteomics
Global Gene Expression Patterns
Networks and Pathways
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Slide 9
Genomics
• Genome sequencing projects: Drosophila, yeast,
human, mouse, Arabidopsis, microbes, …
• Identification of genetic sequences:
• Sequences that code for proteins;
• Sequences that act as regulatory elements.
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Slide 10
Functional Genomics
• The biological role of individual genes;
• Mechanisms underlying the regulation of their
expression;
• Regulatory interactions among them.
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Slide 11
Gene Expression
• When a gene is transcribed (copied to mRNA), it is
said to be expressed.
• The mRNA in a cell can be isolated and examined
using microarrays. Its contents give a snapshot of
the genes currently being expressed.
• Correlating gene expression with conditions gives
hints into the dynamic functioning of the cell.
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Slide 12
Gene Expression Varies
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Slide 13
Networks and Pathways:
Glycolysis, Citric Acid Cycle, and Related
Metabolic Processes
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Slide 14
Bioinformatics at Virginia Tech
Computer Science interacts with the life sciences.
• Joint research with: plant biologists, microbial
biologists, biochemists, cell-cycle biologists,
animal scientists, crop scientists, statisticians.
• Projects: Expresso; NutriPotato; MURI;
Multimodal Networks; Barista; Fusion;
Arabidopsis Genome; Cell-Cycle Modeling
• Graduate option in bioinformatics
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Slide 15
Expresso: A Problem Solving Environment
(PSE) for Microarray Experiment Design and
Analysis
• Integration of design, experimentation, and analysis
• Data mining; inductive logic programming (ILP)
• Closing the loop
• Drought stress experiments with pine trees and
Arabidopsis
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Slide 16
NutriPotato
Microarray technology used to
investigate genes responsible for stress
resistance and for the production of
nutrients in Andean potato varieties.
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Slide 17
MURI
• Some microorganisms have the ability to
survive drying out or intense radiation.
• Using microarrays and proteomics, we are
attempting to correlate computationally the
genes in the genomes with the special traits
of the microorganisms.
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Slide 18
Other Projects
• Multimodal Networks: represent,
manipulate, and identify biological
networks
• Barista: serves software for Expresso, et al.
• Fusion: visualization via redescription
• Arabidopsis Genome Project: mine the
Arabidopsis genome for regulatory
sequences
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Slide 19
Getting Into Bioinformatics at VT
• Learn some biology: genetics, molecular
biology, cell biology, biochemistry (2
courses)
• Study computational biology: CS 5984
• Get involved with bioinformatics research
in interdisciplinary teams
• Work with biologists to solve their problems
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Slide 20
CS 5984: Algorithms in Bioinformatics
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Genetic and physical mapping
Sequence comparison
Sequence alignment
Sequence alignment
Probabilistic models for molecular biology
Fragment assembly
Genome rearrangements
Evolutionary tree (re-)construction
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Slide 21