How to increase the predictive capacity of mathematical models in biology?

Professor Ines Heiland, from the University of Tromsø will show how different approaches and data types can be combined to understand metabolic systems.

Genomes are being sequenced at an enormous speed, but we are still far from understanding the mechanistic basis behind some central physiological processes. Mathematical modelling has been successfully used in recent years to unravel mechanistic details, regulatory principles and interactions between physiological processes. Thereby, modelling approaches have contributed to the understanding of diseases and the development of new therapeutic strategies. So far, the predictive capacity of these models has remained limited, however. A major challenge is the lack of sufficient experimental data enabling appropriate model parametrization. To overcome this problem, we combine different bioinformatics and biomathematical approaches to integrate different data types and expert knowledge, making use of the vast amount of data and information available in the literature and in databases. Using the example of NAD metabolism, a central and essential network of biochemical reactions, I will show how we combine phylogenetic analyses and biomathematical modelling to improve predictions for physiological mechanisms that are relevant for a large number of diseases.