Učni načrt predmeta

Predmet:
Računska sistemska biologija
Course:
Computational Systems Biology
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Tehnologije znanja 1 1
Information and Communication Technologies, 3rd cycle Knowledge Technologies 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-717
Predavanja
Lectures		 Seminar
Seminar		 Vaje
Tutorial		 Klinične vaje
work		 Druge oblike
študija		 Samost. delo
Individ. work		 ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Kristina Gruden
Sodelavci / Lecturers:
prof. dr. Sašo Džeroski
Jeziki / Languages:
Predavanja / Lectures:
Slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second-cycle studies in information or communication technologies or completed second-cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:
Content (Syllabus outline):

Uvod: Koncept sistemske biologije

Eksperimentalni pristopi: Transkriptomika, proteomika, metabolomika

Spremljanje dinamike bioloških sistemov

Analiza 'omskih' podatkov: Postopek analize podatkov, analize podatkov posameznih platform, integrativna analiza podatkov, metode strojnega učenja v sistemski biologiji

Modeliranje strukture in dinamike bioloških
omrežij: Vrste bioloških omrežij, formalizmi za modeliranje omrežij, modeliranje dinamičnih sistemov, kinetično modeliranje v sistemski biologiji

Zgledi in študije primerov

Introduction: The concept of systems biology

Wetlab approaches: Transcriptomics, proteomics, metabolomics

Measurements of biological systems dynamics

Analysis of "omics" data: Data analysis tasks, analysis of single-platform data, integrative data analysis, machine learning and systems biology

Modeling the structure and dynamics of biological networks: Types of biological networks, network modeling formalisms, modeling dynamic systems, kinetic modeling in systems biology

Examples and case studies

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
O. Demin, and I. Goryanin, Kinetic Modelling in Systems Biology. Chapman & Hall/CRC, 2008. ISBN 978-1-5848-8667-9.
E. Klipp, W. Liebermeister, C. Wierling, and A. Kowald. Systems Biology: A Textbook. Wiley-VCH, 2009. ISBN 978-3-5273-1874-2.
S. Choi, Ed. Systems Biology for Signaling Networks. Springer, 2010. ISBN 978-1-4419-5796-2.
S. Džeroski, B. Goethals and P. Panov, Eds. Inductive Databases and Constraint-Based Data Mining. Springer, 2010. ISBN 978-1-4419-7737-3.

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je seznaniti študenta s področjem sistemske biologije, vključno z metodološkimi pristopi v eksperimentalnem delu, analizi podatkov in modeliranju.

Kompetence študenta z uspešno zaključenim predmetom bodo vključevale razumevanje osnovnih pojmov z obeh področij, poznavanje sodobnih metod in njihovo praktično uporabo.

The course objective is to familiarize the student with the field of systems biology, including wet and dry lab methodologies.

The competencies of the students successfully completing this course include understanding of basic concepts from both areas, familiarity with state-of-the art methods, and capability of their use in practical problems.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- pregled obstoječih nalog in metod sistemske biologije
- sposobnost uporabe obstoječih metod na novih problemih
- sposobnost ugotavljanja primernosti različnih pristopov za reševanje specifičnih problemov modeliranja bioloških sistemov

Students successfully completing this course will acquire:
- an overview of existing tasks and methods in systems biology
- the ability to apply existing methods to new problems
- the ability to identify the best methodlogical approach availabile for solving specific problems of modeling biological systems

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, samostojno delo.

Lectures, seminar, consultations, individual work.

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Ustni izpit
50 %
Oral exam
Seminarska naloga
25 %
Seminar work
Ustni zagovor seminarske naloge
25 %
Oral defense of the seminar work
Reference nosilca / Lecturer's references:
1. N Zhou, Y Jiang, TR Bergquist, AJ Lee, BZ Kacsoh, AW Crocker, ..., S Dzeroski, ... The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome biology 20 (1), 1-23, 2019.
2. M Petković, I Slavkov, D Kocev, S Džeroski. Biomarker discovery by feature ranking: evaluation on a case study of embryonal tumors. Computers in Biology and Medicine 128, 104143, 2021.
3. T Stepišnik, ..., P Panov, D Kocev, S Dzeroski, ... Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation NAR Ring, MC Volpe, Cell Death & Disease 13 (1), 2, 2021.
4. BEZJAK, Lara, ERKLAVEC ZAJEC, Vivian, BAEBLER, Špela, STARE, Tjaša, GRUDEN, Kristina, POHAR, Andrej, NOVAK, Uroš, LIKOZAR, Blaž. Incorporating RNA-Seq transcriptomics into glycosylation-integrating metabolic network modelling kinetics : multiomic Chinese hamster ovary (CHO) cell bioreactors. Biotechnology and bioengineering. Apr. 2021, vol. 118, iss. 4, str. 1476-1490
5. LIU, Tianyuan, SALGUERO, Pedro, PETEK, Marko, MARTINEZ-MIRA, Carlos, BALZANO-NOGUEIRA, Leandro, RAMŠAK, Živa, MCINTYRE, Lauren, GRUDEN, Kristina, TARAZONA, Sonia, CONESA, Ana. PaintOmics 4 : new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases. Nucleic acids research. 2022, vol. 50, iss. w1, str. w551-w559