The objective of this three-day course is to study and discuss basic methods, fundamental concepts, and societal challenges of biomedical research. In particular, we will consider models of gene regulation, learn about stem cell technologies, and study the basics of machine learning and its latest biomedical applications.

Day One: we will consider different levels of abstraction of biological systems that enable quantitative descriptions. Such computational models can be used to test different mechanistic hypotheses and predict the dynamics of a system. Day Two: we will look at the architecture of machine learning and deep learning methods that are currently revolutionising a variety of technological and societal aspects of our lives. We will focus on the latest applications in the field of biomedicine. Day Three: we will study the motivation, methods, and opportunities offered by stem cell research and discuss its impact on science and society.

On each day there will be short lectures on selected topics, round-table discussions, and group work. Students will read and present original research literature and are tasked with contextualising it with relevant positions in society and the arts. For those interested, we will implement basic mechanistic and statistical models in Python.

The following three papers are exemplary studies in the research fields we will consider:

Elowitz, M.B. et al., 2002. Stochastic gene expression in a single cell. Science , 297(5584), pp.1183–1186.

Buggenthin, F. et al., 2017. Prospective identification of hematopoietic lineage choice by deep learning. Nature methods, 14(4), pp.403–406.

Esteva, A. et al., 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature. Available at: http://www.nature.com/doifinder/10.1038/nature21056.

The students will be evaluated on the basis of their active contributions to the discussions in the course and the quality of their presentation.

If possible, bring your laptop.