Mechanistic modeling
In our approach to mechanistic modeling, there are two key components: experimental data and a mechanistic hypothesis. In contrast to black-box modelling, we here strive for our mathematical models to be explainable and expand upon our biological knowledge. Therefore, the mechanistic hypothesis is of importance, to base the development of model identified biological processes. The experimental data's role is to validate mechanistic hypotheses and can be of different formats, but time series is typically used. In figure 1 down below, we can see a schematic overview of the iterative process of developing a model. The starting mechanistic explanation and experimental, data are inputs to the process. As a first step, a model is developed to describe the behavior in the data. If an explanation is unable to describe the data, this hypothesis is rejected, and the mechanistic explanation needs to be revisited and reformulated. This loop continues until one or more mechanistic explanations can describe the data, and then we proceed to the core prediction analysis. Here, the value of big data comes to show, as we want to test our model against new data (validation data) that is unseen to the model. The idea is that if the model formulation is close to the true system, then the model hypothesis should be able to explain unseen data from different experiments (on the same system). Here, again, the hypothesis can be rejected as data might not be described and the iterative process continues. Once a model formulation passes this validation data, this is a core prediction. However, new data and hypotheses can always be added to further develop the model formulation, to come closer to a model formulation that can describe the true system.
Key references
Kajsa Tunedal, Tino Ebbers, Gunnar Cedersund,(2025). “Uncertainty in cardiovascular digital twins despite non-normal errors in 4D flow MRI: Identifying reliable biomarkers such as ventricular relaxation rate”, Comput Biol Med, 188:109878 (Take me to the article)
Nicolas Sundqvist, Henrik Podéus, Sebastian Sten, Maria Engström, Salvador Dura-Bernal, Gunnar Cedersund, (2024). “A Model-Driven Meta-Analysis Supports the Emerging Consensus View that Inhibitory Neurons Dominate BOLD-fMRI Responses”, bioRxiv [Preprint], 17:2024.10.15.618416. (Take me to the article)
Nina Grankvist, Cecilia Jönsson, Karin Hedin, Nicolas Sundqvist, Per Sandström, Bergthor Björnsson, Arjana Begzati, Evgeniya Mickols, Per Artursson, Mohit Jain, Gunnar Cedersund, Roland Nilsson, (2024). “Global 13C tracing and metabolic flux analysis of intact human liver tissue ex vivo”, Nat Metab. 6(10):1963-1975.(Take me to the article)
Christian Simonsson, Elin Nyman, Peter Gennemark, Peter Gustafsson, Ingrid Hotz, Mattias Ekstedt, Peter Lundberg, Gunnar Cedersund, (2024). "A unified framework for prediction of liver steatosis dynamics in response to different diet and drug interventions". Clin Nutr., 43(6):1532-1543 (Take me to the article)
Henrik Podéus, Christian Simonsson, Patrik Nasr, Mattias Ekstedt, Stergios Kechagias, Peter Lundberg, William Lövfors, Gunnar Cedersund, (2024). "A physiologically-based digital twin for alcohol consumption-predicting real-life drinking responses and long-term plasma PEth". NPJ Digit Med., 7(1):112. (Take me to the article)
Key personnel
Gunnar Cedersund
Elin Nyman
William Lövfors
Henrik Podéus
Christian Simonsson
Jona Ekström
Nicolas Sundqvist
Oscar Silfvergren
Tilda Herrgårdh
Kajsa Tunedal