Biology has generated huge amounts of data in particular fueled by the high throughput technologies emerging in the last years. This makes the question of how all these discovered ingredients of life work together to form living systems more and more urgent. Systems Biology aims to answer this question, it tries to develop an integrative, more holistic view of life. It tries to discover with a combination of experiments and mathematical approaches how living organisms function as a consequence of the interplay of all their components. This lecture will introduce basic concepts, ideas and techniques of Systems Biology. We will discover how we can model biochemical pathways, how cells can integrate a multitude of signals to react to their environments, what drives the evolution of organisms and finally how organisms come together to form whole populations and ecosystems. The overall goal of this lecture is to complement the very successful, molecular view of biology with more generalizing and conceptual ideas. Accordingly, this lecture will be of interest for students of all life sciences (like Microbiology, Biochemistry, Molecular Cell Biology or Ecology). Only minor previous math knowledge is assumed.

This course is aimed at biology students with little or no programming experience. It aims to provide an overview of computational methods that play important roles in solving diverse biological problems. The course will cover different applications, such as data analysis in high-throughput CRISPR screens, analysis of LC-MS based proteomics data and metabolomics data, machine learning with decision trees and random forest models, comparative genomics. Overall, the course will provide a starting point for writing small programs that allow students to analyze different types of biological data. The class will use Jupyter Notebooks and the Python programming language.
Christoph will teach the Machine Learning part.