Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After the course students will have a comprehensive understanding of fundamental methodological concepts underlying modeling of biological networks and systems. Students will learn to choose appropriate modeling methods for a variety of small- and large-scale problems as well as for different types of experimental data. Students will learn to apply various computational and statistical modeling methods in real interdisciplinary biological problems and have sufficient knowledge to explore the topic further.
Schedule: 12.01.2021 - 25.02.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Harri Lähdesmäki
Teacher in charge (applies in this implementation): Harri Lähdesmäki
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Mathematical and statistical models of biological molecular-level networks, such as gene regulation, epigenetics, signaling, and metabolism. Models covered in the course include stochastic chemical reaction networks, ordinary differential equations, Bayesian and dependency networks, regression models and random networks. Statistical methods for inference of networks from data, and prediction using the models. Mainly probabilistic and machine learning models.
Assessment Methods and Criteria
Examination and exercises/assignment problems.
24 + 24 (4 + 4)
To be specified in MyCourses at the start of the course.
Substitutes for Courses
Replaces former course CS-E5880 Modeling Biological Networks.
Basic mathematics, statistics and computer science/programming courses. Basic bioinformatics courses help.
- Teacher: Harri Lähdesmäki