Bioinformatics III: Structural Bioinformatics and Gene Analysis (2KV)
| Course no.: |
365.029 |
| Lecturer: |
Noura Chelbat
|
| Times/locations: |
Tue 03.03. 15:30-17:00, room T 111
Tue 10.03. 9:15-11:00 and 15:30-18:45, room T 111
Tue 05.05. 15:30-18:45, room T 111
Tue 12.05. 15:30-18:45, room T 111
Tue 19.05. 15:30-18:45, room T 111
Tue 26.05. 15:30-18:45, room T 111
Tue 09.06. 15:30-18:45, room T 111
Wed 17.06. 15:30-18:45, room T 112
Tue 23.06. 15:30-17:00, room KG712 (Brainstorming) |
| Mode: |
KV, 2h, Block |
| Registration: |
KUSSS |
Lecture notes:
PDF (5.6 MB)
Slides:
PDF_1 (3.3.2009)
PDF_2 (10.3.2009)
PDF_3 (5.5.2009)
PDF_4 (12.5.2009)
PDF_5 (19.5.2009)
PDF_6 (26.5.2009)
PDF_7 (9.6.2009)
PDF_8 (17.6.2009)
Contents:
Data bases for 3D structures, Molecular Viewers, Structure prediction, Threading,
ab initio prediction, molecular dynamics, structural alignments, protein folding,
protein classification, Motif search, gene expression profiles, Microarray technique,
Single Nucleotide Polymorphism, Gene selection, Epigenomics, Pathways, etc.
Motivation:
The course "Bioinformatic III" gives an introduction to two main topics of bioinformatics:
Structural Bioinformatics and Microarray technique together with gene expression profiles.
A main topic in structural bioinformatics is the prediction of the 3D structure. This could
be done from the sequences obtained from the genome sequences which would mean that the
function of the genes in the genome can be inferred. The Microarray technique is currently
the major source of information about the working of the cell and evolved to one of the
major topics in Bioinformatics.
In Structural Bioinformatics, the 3D structure of proteins, DNA and RNA is analyzed and
predicted from the primary structure. Topics include structural alignments, protein folding,
prediction of 2D/3D structures, 3D viewer and molecular dynamics.
One of the goals of Structural Bioinformatics is to give computational approaches to predict
and analyze the spatial structure of macromolecules like proteins and nucleic acids.
Understanding their 3D structure is crucial for understanding their function. Direct
applications could be oriented to medical fields and pharmacological research. Especially
for drug design, one of the most important goals is to determine which groups of ligands
bind to a particular part of a protein and which do not, which properties are these proteins
sharing, which proteins could be used as target, which drugs could be constructed and be
used as virus inhibitors, etc.
Homology and comparative modelling can be used for the detection of 3D structure and hence
for function inference, so similar structure can imply similar function. Aspects of protein
functions can be obtained by molecular mechanics and molecular dynamics like force-field.
When no detections are raised by sequences comparisons then approaches like
sequences-to-structure-fitness should be used which is called threading.
In the field of Microarray technique and gene expression profiles the expression states
of genes in cell is the focus of interest. The gene expression profile reports how much
mRNA is found in the cell which tells what proteins and how many are currently produced
in the cell. This in turn tells what the cell is doing currently. Microarrays are the
biotechnological tools to measure gene expression profiles. The DNA Microarray Technologies
such as cDNA arrays and oligonucleotide arrays provide means of measuring tens of thousands
of genes simultaneously (a snapshot of the cell) as a large scale high-throughput method
for molecular biology experimentation. Topics in microarray analysis include normalization
and summarization algorithms, statistical approaches, biotechnical background knowledge,
SNP arrays and topics in gene expression profile analysis covering gene selection,
classification and prediction based on the gene expression profile, spurious correlations,
pathway extraction, etc.
One of the goals of Microarray Technology is the detection of genes that are differentially
expressed in tissue samples like healthy and cancerous tissues to see which genes are
relevant for cancer. It has important applications in pharmaceutical and clinical
research and helps in understanding gene regulation and interactions.
The information obtained by recognizing genes that share patterns of expression and
hence might be regulated together could be used to draw genetic pathways.
