Courses in previous years: [ 2006 ][ 2007 ]
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These pages are not any more updated. Please, see web pages of Department of Information and Computer Science (ICS): http://ics.tkk.fi/en/studies/.
Kaski, Department of Information and Computer Science, Helsinki
University of Technology|
|Assist. Prof. Sophia Kossida , Academy of Athens, Biomedical Research Foundation, Bioinformatics & Medical Informatics Team |
|Assistant||M.Sc. Ilkka Huopaniemi|
|Credits (ECTS)||5 or 7|
|Semester||Spring 2008 (period IV)|
|Sessions||Two intensive days 13-14.3. A328. Thereafter on Wednesdays at 9-11 and Thursdays at 12-14 in room A328 (at the Computer Science and Engineering building).|
|Registration||TKK students: WebTopi, others: send mail to t616070 at cis.hut.fi.||Webpage:||http://www.cis.hut.fi/Opinnot/T-61.6070/|
|t616070 at cis.hut.fi|
Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. The term "proteomics" was coined to make an analogy with genomics, the study of the genes. The proteome of an organism is the set of proteins produced by it during its life. Proteomics is often considered a main next step in the study of biological systems, after genomics. It is much more complicated than genomics, mostly because while an organism's genome is rather constant, a proteome differs from cell to cell and constantly changes through its biochemical interactions with the genome and the environment.
Proteomics research is undergoing excessive growth. Nearly every major biotech and pharmaceuticals firm has implemented a proteomics program. Functional proteomics, the study of protein function and identification of protein interactions, is playing a major role in drug discovery, biomarkers, molecular diagnostics, and antibody therapies. Today's estimation of the number of human genes is just 20,000 to 25,000. These genes give birth to around 100.000 protein transcripts. Posttranslational modifications turn the number of these proteins to about 1.000.000. This demands proteomic research to develop a wide range of software tools to manage the data arising from the need to interpret experiments, resolve protein structures, study protein-protein interactions and finally to place each protein to its functionally correct position in the rapidly expanding proteome network atlas.
This course is designed to introduce computational and statistical concepts and tools necessary to analyze proteomics data, mainly mass-spectrometry-based measurement data. The skills learned will also be applicable to other problems involving large data sets, such as gene expression data, metabolomics, and more generally in statistical data mining.
The course will be most useful for graduate-level (after bachelor) or doctoral students of bioinformatics or related fields. Mathematically oriented biology and medical students are very welcome as well. The modeling methodologies are very general, and useful also for other students of computer science, mathematics and physics.
In the seminar part, every participant gives one lecture/presentation. Passing the course with 7 credit points requires performing the following tasks:
Instructions for the individual tasks are given here, exercises here, and examples of the exercises of the 2007 course in old exercise problems. Leaving out the project work but passing the first four requirements results in 5 credit points. The course will be graded so that 60% of the grade is based on the presentation (including the exercise task) and 40% on the project work. If one solves almost all (90%) exercise problems then they have a weight of 10% towards the best grade, and solving at least half of them is required for passing.
Some basic course on machine learning helps significantly in understanding the models, but sufficient knowledge of mathematics (probability, statistics, linear algebra etc) should also be enough. Basic knowledge of bioinformatics or computational biology is strongly advisable.
The topics and material for the presentations will be tailored for each participant in the beginning of the course.
Below is a preliminary schedule for the course. The topics and the material of the remaining presentations will be added when they have been fixed.
|Time||Lecturer||Subject and material|
|13-14.3.||Sophia Kossida, Sami Kaski||
|Thu 27.3.||Paula||Quantitative proteomics|
|Wed 2.4.||Laszlo||Phylogenetic trees|
|Thu 3.4.||Tomi||Protein-protein interactions|
|Wed 9.4.||Jose||Integration of mRNA expression data with proteomics|
|Thu 10.4.||Jaakko, Gopal||
Coronary heart disease related proteomics(Jaakko)
|Wed 23.4.||Abhishek, Hitomi||
|Thu 24.4.||Taru||Integrating proteomics and metabonomics data|
|Wed 30.4.||Lauri, Laxman||Gene ontology(Lauri)|
You are at: CIS → T-61.6070 Special course in bioinformatics I: Modeling of biological networks
Page maintained by t616070 (at) cis.hut.fi, last updated Tuesday, 19-Aug-2008 10:51:04 EEST