Vorlesung: Introduction to Biomedical Systems Modeling and Data Science - Details

Allgemeine Informationen

Veranstaltungsname	Vorlesung: Introduction to Biomedical Systems Modeling and Data Science
Veranstaltungsnummer	INF-0516
Semester	SS 2025
Aktuelle Anzahl der Teilnehmenden	16
Heimat-Einrichtung	Lehrstuhl für Modellierung und Simulation biologischer Prozesse - Prof. Dr. Andreas Raue
Veranstaltungstyp	Vorlesung in der Kategorie Lehre
Erster Termin	Donnerstag, 24.04.2025 10:00 - 11:30, Ort: (Seminar room 1054, Building N)
Voraussetzungen	• Basic understanding of biology, mathematics (calculus, linear algebra) • Basic knowledge of Python, Jupyter Notebooks and libraries (NumPy, SciPy, Scikit-learn, Pandas, Matplotlib, etc.) and virtual environment handling (Conda) • Help will be provided to develop skills during the “hands-on” sessions
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile	Ja
Hauptunterrichtssprache	englisch
Weitere Unterrichtssprache(n)	deutsch
Literaturhinweise	For further reading, not a blueprint for this lecture! - "Introduction to Machine Learning" by Ethem Alpaydin - "Machine Learning" by Tom M. Mitchell - "Introduction to System Biology" by Edda Klipp - "A First Course in Systems Biology" by Eberhard O. Voit
ECTS-Punkte	5

Lehrende

• Prof.Dr. Andreas Raue

Tutor/-in

• Jiedan Xiao

Räume und Zeiten

(Seminar room 1054, Building N)

Donnerstag: 10:00 - 11:30, wöchentlich (10x)

Donnerstag, 24.04.2025 10:00 - 11:30

Studienbereiche

• Medizinische Fakultät > Medizininformatik
• Fakultät für Angewandte Informatik > Informatik
• Mathematisch-Naturwissenschaftlich-Technische Fakultät > Institut für Mathematik
• Medizinische Fakultät
• Fakultät für Angewandte Informatik
• Mathematisch-Naturwissenschaftlich-Technische Fakultät > Institut für Physik
• Mathematisch-Naturwissenschaftlich-Technische Fakultät > Institut für Mathematik > Bachelor Data Science

Modulzuordnungen

• Data Science Bachelorstudiengang Data Science, 6. Version gültig ab SS 2025 > F. Wahlbereich > INF-0516 - Introduction to Biomedical Systems Modeling and Data Science > Introduction to Biomedical Systems Modeling and Data Science (Lecture)
• Mathematik und Informatik Bachelorstudiengang Mathematik und Informatik, 4. Version gültig ab WS 2024/25 > F. Wahlbereich > INF-0516 - Introduction to Biomedical Systems Modeling and Data Science > Introduction to Biomedical Systems Modeling and Data Science (Lecture)
• Medizinische Informatik B.Sc. Medizinische Informatik, PO 2018, 13. Version gültig ab WS 2024/25 > Medizinische Informatik > INF-0516 - Introduction to Biomedical Systems Modeling and Data Science > Introduction to Biomedical Systems Modeling and Data Science (Lecture)
• Data Science Bachelorstudiengang Data Science, 5. Version gültig ab WS 2024/25 > F. Wahlbereich > INF-0516 - Introduction to Biomedical Systems Modeling and Data Science > Introduction to Biomedical Systems Modeling and Data Science (Lecture)
• Mathematik und Informatik Bachelorstudiengang Mathematik und Informatik, 5. Version gültig ab SS 2025 > F. Wahlbereich > INF-0516 - Introduction to Biomedical Systems Modeling and Data Science > Introduction to Biomedical Systems Modeling and Data Science (Lecture)
• Medizinische Informatik B.Sc. Medizinische Informatik, PO 2018, 14. Version gültig ab SS 2025 > Medizinische Informatik > INF-0516 - Introduction to Biomedical Systems Modeling and Data Science > Introduction to Biomedical Systems Modeling and Data Science (Lecture)

Kommentar/Beschreibung

Description:
This interdisciplinary course is designed to provide bachelor students with a basic understanding of computational techniques in systems modeling and data science applied to biomedical problems. Students will learn to model biological systems and analyze data using both mechanistic models and statistical/machine learning models. The course includes review and discussion of the theoretical foundations, practical applications and exercises, and hands-on projects. Prerequisites include a basic understanding of biology, mathematics (calculus, linear algebra), and programming (python). The goal of this course is to prepare students for a career path as entry-level scientists in biomedical, biotechnology or pharmaceutical industry, or for continuing their academic training to acquire master or PhD degrees.

Objectives:
• Understand the foundational principles of systems modeling and data science.
• Develop abilities to apply mathematical and computational methods to analyze biomedical systems and datasets.
• Provide hands-on experience in implementing and interpreting mechanistic and statistical/machine learning models using case studies.
• Foster critical thinking and problem-solving skills in biological and machine learning applications

Contents:
Part 1: Data Science
• Introduction Biomedical Data Science, Omics, statistics and data visualizations
• Differential (gene expression) analysis, statistical testing, correlation
• Clustering, heat maps, dendrograms, correlation
• Linear models, dimensional reduction techniques
• Gene set enrichment analysis & supervised learning methods
• Outlook advanced ML, “deep” learning
Part 2: Systems Modeling
• Introduction to mechanistic models and dynamical systems
• Formalism for ordinary differential equation models
• Numerical solvers and modeling biochemical reactions
• Cellular signaling models and model parameterization