Computational Molecular Medicine

General data

Course ID:	1000-2M14OMM
Erasmus code / ISCED:	11.3 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Computational Molecular Medicine
Name in Polish:	Obliczeniowa medycyna molekularna
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Prerequisites:	Introduction to computational biology 1000-2N03BO
Short description:	The most important models and computational methods used in analysis of molecular medical data. Basic algorithms and mathematical models for medical diagnostics.
Full description:	Program: 1 Introduction to molecular biology and medicine. 2 Basic biotechnology: microarrays, mass spectrometry, sequencing. 3 Array of Comparative Hybridization: algorithms for data analysis. 4 Structural mutations and genome architecture. 5 Genetics: Mendelian diseases. 6 Transcriptomic microarrays: algorithms for data analysis. 7 Transcriptomic microarray in diagnosis. 8 Mass spectrometry: algorithms for automatic interpretation of signals. 9 Mass spectrometry in medical diagnostics. 10 Modeling the molecular signaling pathways. 11 Methods for analysis of models . 13 Oncogenic signaling pathways.
Bibliography:	Darren Wilkinson, Stochastic Modelling for Systems Biology, Chapman & Hall, 2011. K.A Do, Z.S. Qin, M. Vanucci, Advances in Statistical Bioinformatics Models and Integrative Inference for High-Throughput Data, Cambridge University Press, 2013.
Learning outcomes:	knowledge: 1. has in-depth knowledge of the fields of mathematics necessary for bioinformatics (probability, statistics, differential equations) (K_W01) skills: 1. can prepare oral presentation in Polish and/or English on computer science or interdisciplinary topic (K_U11) 2. can prepare (also in English) scientific report (K_U13) Competence 1. knows the limits of his own knowledge and understands the need for further education, including the acquisition of interdisciplinary knowledge (K_K01) 2. can precisely formulate questions that deepen their understanding of the topic or find missing elements of reasoning (K_K02)
Assessment methods and assessment criteria:	Presentation and the project, which completion are necessary to take the exam (oral exam).

This course is not currently offered.

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