Enabling mechanistic understanding of cellular dynamics through mathematical modelling and development of efficient methods

Abstract

Cell biology is complex, but unravelling this complexity is important. For example, the recent COVID-19 pandemic highlighted the need to understand how cells function in order to develop efficient vaccines and treatments. However, studying cellular systems is challenging because they are often highly interconnected, dynamic and contain many redundant components. Mathematical modelling provides a powerful framework to reason about such complexity.

In the four papers underlying this thesis, our aim was twofold.The first was to unravel mechanisms that regulate cellular dynamic behaviour in the model organism Saccharomyces cerevisiae. In particular, by developing single-cell dynamic models, we investigated how cells respond to changes in nutrient levels. We identified mechanisms behind the reaction dynamics and uncovered sources of cell-to-cell variability. Additionally, by developing reaction-diffusion modelling, we studied the size regulation of self-assembled structures and demonstrated how the interplay of feedback mechanisms can regulate structure size. Our second aim was to develop methods and software to facilitate efficient modelling. Modelling often involves fitting models to data to verify specific hypotheses, and it is beneficial if models inconsistent with the data can be discarded rapidly. To this end, we developed software for working with single-cell dynamic models that, in contrast to previous methods, imposes fewer restrictions on how cell-to-cell variability is modelled. Moreover, we developed and evaluated software for fitting population-average dynamic models to data. This software outperforms the current state of the art, and to make it accessible, we released it as two well-documented open-source packages. Taken together, this thesis sheds light on fundamental regulatory mechanisms and introduces software for efficient modelling.