LEARNING OUTCOMES
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.
Credits: 5
Schedule: 10.01.2023 - 23.02.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Harri Lähdesmäki
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
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
valid for whole curriculum period:
Examination and exercises/assignment problems.
Workload
valid for whole curriculum period:
24 + 24 (4 + 4)
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
3 Good Health and Well-being
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III
2023-2024 Spring III