Details

A summer Master’s course has just launched, combining expertise in biology and computer science into a programme on computational modelling in neuroscience and systems biology. The 16-week directed study course aims to equip students with the key competencies of quantitative reasoning, modelling and simulation, and experience in interdisciplinary collaboration—enabling them to advance their careers by mastering the fundamental skills needed to address a broad range of scientific and societal problems today.

Biological systems are dynamic, interactive, and complex. Researchers studying systems at every level—molecular, cellular, organismal, and ecosystem—are increasingly turning to computational data analysis and modelling techniques in order to describe and make sense of living systems. The data currently being generated in huge amounts and at astonishing speed is further accelerating the pace of change—creating an urgent need for trained graduates who can integrate this “big data” into models that will uncover the principles underlying potentially complex and large-scale systems.

The Computational Modelling in Neuroscience and Systems Biology master’s course complements lectures on basic mathematical and computational modelling approaches employed in biological systems with hands-on activities—providing graduates of this programme with the quantitative toolkit needed to keep pace with the data revolution sweeping the sciences.

Drawing on classic models that have revolutionised several fields of the biological sciences, the course will engage students with the impact of computer science in biology and the importance of using approaches from both disciplines. One case study will focus on the dynamic modelling of neural networks, which was the precursor to the boom in artificial intelligence that is transforming the role of technology in our lives. Other examples include the dynamic modelling of gene control and molecular signal transduction in cells. A mini project at the end of the semester will give biology and computer science students the opportunity to work together and hone the collaborative skills needed to succeed as science becomes increasingly interdisciplinary and global.

Course instructors Tatjana Petrov, a professor in Modelling of Complex Self-Organizing Systems in the Department of Computer and Information Science at the University of Konstanz, and Jacob Davidson, a researcher in the areas of computational neuroscience and animal behaviour in the Max Planck Department of Collective Behaviour in Konstanz, say that students from both disciplines will benefit from this interdisciplinary course.

Prof Petrov: “With today's measurement technology and computing power, unravelling the secrets of life with potentially novel diagnostic and therapeutic applications seems closer than ever, but it requires joining forces between disciplines. Computer science is one of the key drivers of this future, not only to process data or simulate models even faster, but also to design novel domain-specific modelling languages that can describe the often complex nature of biological systems.”

“In this course, I expect that computer science students interested in theory will be fascinated to learn about the models of computation that are implemented in nature, such as gene regulation or neuronal spiking. Moreover, they will get to see where some classical methods of computer science—originally thought to aid software or hardware design—can now improve our reasoning about biological systems. On the other hand, for the more application-driven students, the course will put them in a unique position to use their computational skills to leverage the fast-paced growth in this booming field.”

Dr Davidson: “Using approaches like statistics, quantitative analysis of dynamic systems, and mathematical modelling is becoming standard in more and more fields in the biological sciences. But many biology curriculums haven’t caught up, and the computational and quantitative approaches most graduates come away with don’t go beyond elementary statistics. Graduates of this master’s course will come away with the skills to deal with the models that are quickly becoming mainstream in biological sciences, and that promise to contribute to discoveries in research, industrial applications, and societal progress.”

ABOUT THE COURSE INSTRUCTORS

Tatjana Petrov
Tatjana’s research lies at the interface of theoretical computer science and mathematical modelling, and her favourite application of interest are biological systems. Prior to becoming a Junior Professsor in the Department of Computer and Information Science, Tatjana was a postdoctoral Fellow at IST Austria, and she obtained her PhD in 2013 at ETH Zürich.  During her PhD, she held long research stays at École polytechnique fédérale de Lausanne and at Harvard Medical School.

Jacob Davidson
Jacob is a theoretical biologist currently doing postdoctoral research at the Max Planck Institute for Ornithology, Department of Collective Behaviour.  After completing his PhD at the University of Michigan, he moved to the Center for Neuroscience at University of California, Davis to work as part of an interdisciplinary collaboration with experimental biologists at Stanford University.  His interest in teaching computational modeling stems from his time at the Marine Biological Laboratory in Wood’s Hole in 2015 and 2016, where he was an assistant for the course Methods in Computational Neuroscience. His current research is in the areas of quantitative behavioral analysis, and collective motion and decision-making.