Modeling biochemical reaction networks in complex intracellular environments

Whether one aims to design treatments for diseases such as cancer or diabetes, engineer cells to produce valuable biochemicals sustainably, or to grasp the behavior of living organisms, it is essential to understand how cells react to genetic, environmental, and biochemical perturbations. Responses of living systems to such perturbations are tied to the dynamics of the biochemical networks implementing the various biological functions necessary for its survival. Thus, knowledge about the dynamics of these biochemical networks is crucial to understand how living entities react to changes in their environment, genes, or biochemistry. The environment within cells is filled with proteins, lipids, polysaccharides, RNA, and DNA, creating various kinds of structures such as droplets, aggregates, and filaments and occupying up to 40% of the intracellular volume. Many of the reactions within the biochemical reaction networks can only be studied in detail when isolated from the intracellular environment. Interaction of the reactants with these structures and other molecules within the cellular volume affects the dynamics properties of the biochemical reactions. The data obtained from experiments that measure the reaction dynamics isolated from the intracellular environment in a dilute setup, omit the effects originating from the interactions with this complex intracellular environment. This discrepancy can result in misinformed models unable to capture the cellular responses. Theoretical and computational models can provide insight into how the reaction kinetics is altered due to the structures inside the cell.

In this thesis, we studied the behavior of reactions confined to different structures inside the cell. Therefore, we focused our efforts on the dynamic capture of chromatin-binding proteins as well as the effects of macromolecular crowding on enzyme kinetics and enzyme reaction networks. Firstly, we used computational modeling and parameter estimation methods to identify the binding mechanism of a heterochromatin effector protein binding to a posttranslational modification mark. We then identified the key parameters of this binding mechanism to investigate the potential effects of the intracellular environment on the binding behavior. Secondly, we investigated the reaction kinetics of enzymes in a solution of macromolecules, such as other enzymes, proteins, DNA, and others. We, therefore, developed a new computational framework that allowed us to parameterize approximate reaction kinetic based on particle simulations. This method allowed us to show that maximal enzyme rates and Michaelis-Menten constants are reduced up to 10 fold upon introducing macromolecules into the system. We continued the analysis of crowded enzyme kinetics by developing a new theoretical model of crowded diffusion-controlled reaction networks. We then used this model to study the role of diffusion in crowded enzyme kinetics. Thirdly, we combined the approximate kinetics with the theoretical model to investigate the impact of crowding on prototypical metabolic networks consisting of multiple enzymatic reactions. Our results suggest that crowding can have significant and unintuitive effects on the sensitivities of the reaction network, making a strong for the application of the derived methods in large scale metabolic models. Finally, we outline the path towards application-specific in vivo like models from the integration of revaluated in vitro data.