Modelling living systems - From foundational problems to applications

The increased use of digitalization technologies in life science changes the way life science research is done. State of the art experimental techniques now routinely produce very large datasets of which the interpretation is unclear. This does not only increase our ability to interrogate the natural world, but also affects the research questions. This changes life-science research, but also our view of what a living system is. The practical consequences of this are still unclear.

Registration form: 

Based on the positive feedback from former events, Centre for Digital Life Norway organize this workshop for the third time.

The workshop is led by Dominique Chu from the University of Kent, UK, and professor Roger Strand from the Centre for Digital Life Norway, NTNU. Participation is open to  established and early career researchers researchers, students and postdocs. The workshop will provide ample space for participants to present and discuss in depth challenges related to their research. Active participation will be encouraged. By the end of the workshop, partiucipants will have a clearer idea of hidden assumptions in their models. This will not only provide participants with a more critical view of their own practices, but hopefully also enable them to improve the relevance of their research.

Detailed description:

There are many challenges of using both biological and mathematical models in life science.  Living systems are complex and variable, very different from simple mechanical systems where systemic models is more established. Living organisms are thermodynamically open and hold processes that spans both size and time. In addition, they have self-regulating, replicating, and evolving properties, that are adapted to sustain environmental and internal changes. How can we deal with the fundamental difficulties of modelling living systems?

New digitalization technologies can provide a vast number of measurements and data from the model systems, and artificial intelligence (AI) and machine learning (ML) are increasingly used to understand and predict systemic responses. Such methods hold high promises for the future of life science, but they also have their limitations. How do we evaluate the data that are used and produced through AI/ML approaches and how do we address the problem of model bias and algorithmic design when applied to living, complex systems?

The aim of this workshop is to create a forum where such fundamental issues and difficulties can be reflected on and discussed openly.

Participants are expected to present their own models for discussion. This will be a central part of the workshop and increase involvement and learning. Participants will also be requested to submit material and questions to the organizers before the workshop so that the discussion can be well prepared. The maximum number of participants are 20. As there is a limited number of places available, a confirmation e-mail will be sent to those that get a sport in due time before the workshop.


From attending the workshop, we expect that the participants:

  • gain insight into the (sometimes hidden) aspects and considerations of their model
  • can openly evaluate the advantages, and possibly disadvantages, of their models
  • are able to identify shared interest and issues with the other participants


Tentative program:


Thursday 15. September 

11:30-12:45   Introduction over light lunch 

13:00-14:30   Overview of modelling approaches and architectures

14:30-17:00   Challenges of modelling in life science


Friday 16. September 

09:00-10:30  Purpose and quality criteria for models. Explanatory power, prediction and engineering 

11:00-12:00  Analysing participants’ models – I 

12:00-12:45  Lunch 

13:00-14:30  Analysing participants’ models – II 

14:30-15:00  Summary and conclusion


For questions about the workshop, contact:

Marta Eide, 

Anders Braarud Hansen,

Tags: Workshop, Forskerskolen
Published May 16, 2022 2:47 PM - Last modified May 20, 2022 6:00 PM