1. What exciting topics are you currently working on?

We are focusing with all projects on two types of data: arising from patients in the clinic, as well as molecular interaction data. In both cases, we are trying to identify pattern in data, to speculate about mechanisms and rules to explain observations.. Making inferences in face of uncertainty is what we get excited about :)

2. How would you summarise your career path?

I studied mathematical modeling of dynamical systems, which motivated me to focus on molecular and cellular processes. In my PhD, I focussed on dealing with various types of uncertainty, arising from complex systems, leading to very short time series or sparse data. These problems are common in the life sciences, which motivated me to focus on collaborations in biomedicine. At the time, my choices for collaborations and topics were unusual and high risk. I am glad to have focussed on interdisciplinary collaborations and I am grateful for the experiences I had in working with colleagues in various fields and across the globe.

3. Where do your best ideas emerge from?

In discussion and from reading across a wide range of topics. I then try to translate or transfer ways of thinking or methodologies from one domain to another. Nowadays, I may have an initial idea but the discussion with young colleagues, that are smarter than me, will generate most of the best ideas - coming from them.

4. Who or what had or has had the strongest positive influence on your career and work?

I was very lucky to have received the support of senior colleagues, who became friends and advisors. I am now trying to copy them to provide PhD students and postdocs with a supportive environment. Thinking about it, those people who had the strongest influence on me, where somewhat unusual personalities. I would now recommend young scientists to go out and seek contact with people they find interesting and interact with them. The time, in which others can have influence, passes quickly.

5. What do you consider the greatest challenge or hurdle for progress in your field?

Understanding organizing principles across levels of structural and functional organization. We are really good in "zooming in", generating more and deeper insights into molecular details, but we do quite poorly in "zooming out", seeing the larger picture of how all those molecular processes interact so as to generate tissue and organ level phenomena. My dream is to discover a law-like principle underlying whole-part relationships in complex, living systems.

6. What are, in your opinion, the opportunities, directions or decisions that are vital to progress neurosciences?

Around 2016, new methodologies from AI, machine learning and data science emerged, that are now dominating many research directions. For us, it is the lack of sufficiently rich datasets, and the handling uncertainty arising from this sparse data, drives our research in data science. Many biomedical or disease-related phenomena are processes, that is, dynamical systems. If temporal changes matter for what is to happen, then there is no way round using systems theory. This makes many things more complicated, including the generation of data. It is of vital importance that we do not shy away from spelling this out and deal with it. AI will help us to fish for pattern in data, and support decisions but if the things we look at, are nonlinear dynamical systems, we should not expect many shortcuts to understand them.

7. Tell us what would have to happen in your work for you to say “A dream has come true!”?

My dream is to discover a law-like organizing principle. But 'a' dream coming true, would be if that argument about a principle of, say tissue organization, is changing the way people think about the phenomena under consideration. I most admire scientists who influenced our way of thinking.

8. What was your greatest experience as a scientist?

There is no single event to pick out but the whole process of becoming a scientist, being allowed to do research, is a wonderful experience. I consider myself very lucky and happy being a scientist. I was a dreamer as a child and as an apprentice in industry my dream was to return to school and go to University. When I reached the end of my University degree, I was desperate not to go back to industry and my dream was to become a scientist. Now, with an established research group around me, I enjoy working in a team and providing others with those opportunities I longed after when I was younger. The greatest experience is thus to listen and talk to smart people.

9. What has been your biggest scientific failure so far, and how do you deal with failed experiments and defeats?

With regard to my ultimate goals, failure is an almost daily experience. Success in science is going from failure to failure, without loosing enthusiasm. But I am not defining my happiness in terms of those dreams but instead make sure that there are short term goals that generate satisfaction. It is however also true that I wasted many months on topics and ideas that turned out wrong. Receiving rejections for grant proposals, where I was convinced that they are really good, and we put a lot of work into, is painful. Fortunately, I have learned to forget about these experiences, and move on - to persist, and not to give up is probably a key skill here.

10. With which historical person, politician or celebrity would you like to have a dinner and discuss your work?

Unfortunately, for scientists, mathematicians and philosophers who I admire for their work (think of Arthur Schopenhauer and Kurt Gödel), their personality does not suggest a happy dinner experience. I would thus go for meeting with Angela Merkel, to learn from her how one could make a meaningful contribution to respond to the crises that we currently experience (climate and spread of populism).

Prof. Olaf Wolkenhauer
Institute of Computer Science
Department of Systems Biology and Bioinformatics
University of Rostock
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