Minor in Computational Biology, 25 cr
Type of the study module
Intermediate Studies
Contact
Andre Sanches Ribeiro, Olli Yli-Harja, Frank Emmert-Streib
Learning Outcomes
| - | Have basic knowledge of Cell and Molecular Biology, Biotechnology, Signal and Image Processing. |
| - | Implement models of biological systems. |
| - | List and describe the main research areas of Computational Biology.
|
| - | Provide examples of how computational models are used in the study of biological systems. |
| - | Use computational tools, such as Matlab or R, to implement and solve problems in biological data analysis, such as image and data analysis. |
Content
Compulsory courses
| Course | Credit points | Class |
| SGN-52406 Models of Gene Networks | 3 cr | IV |
| SGN-56007 Laboratory Course in Computational Biology | 3 cr | V |
| Total | 6 cr |
Optional Compulsory Courses
Depending on their background, the students are advised to select from the list of optional courses, one of the following two study blocks:
Must be selected at least 19 credits of courses
| Course | Credit points | Alternativity | Class |
| SGN-11006 Basic Course in Signal Processing | 5 cr | 2 | V |
| SGN-12006 Basic Course in Image and Video Processing | 5 cr | 2 | V |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | 5 cr | 1, 2 | V |
| SGN-51006 Biology of the Cell | 3 cr | 1 | IV |
| SGN-52606 Processing of Biosignals | 5 cr | 1, 2 | IV |
| SGN-53007 Computational Diagnostics | 5 cr | 1, 2 | IV |
| SGN-53206 Cell Culturing, Microscopy and Cell Image Analysis | 3 cr | 1 | IV |
| SGN-53807 Methods in Single Cell Biology | 3 cr | 1 | IV |
| SGN-84007 Introduction to Matlab | 1 cr | 2 | IV |
1.
Select 19 credits.
Alternativity 1 is intended for students with a background in Signal Processing or Software Engineering.
2.
Select 19 credits.
Alternativity 2 is intended for students with a background in Biotechnology, Biomedical Engineering, or Cell and Molecular Biology.
Additional information
The Minor in Computational Biology provides knowledge on how to design and implement models of biological systems. Also, it teaches how to use computational tools for biological data analysis.
The Minor in Computational Biology is a valuable, complementary knowledge for students of Signal Processing and Software Engineering that aim to apply their efforts to study biological/medical topics as well as for students of biological/biotechnological/medical degrees that aim to perform modeling of biological systems or make use of computational methods, e.g. from Signal Processing, such as image and statistical analysis.
The Minor in Computational Biology is highly multi-disciplinary in that it provides knowledge in Computational Biology, along with knowledge from the areas of Signal Processing, Machine Learning and Experimental Biology, including the emerging area of Single Cell Biology.
Only intended as a minor