Investigating crowded metabolism: A molecular particle approach

Conference Dates

July 16-20, 2017


The intracellular environment is a crowded place. About 40% of its volume is occupied by macromolecules. In this dense mixture of proteins, lipids, polysaccharides, RNA and DNA, all biochemical reactions must take place. The composition of the mixture depends considerably on the organism in its environment, but even within the cell, the composition varies between and within compartments. When we observe a metabolic reaction in this mixture, the presence of all the other molecules has two counteracting effects: On the one hand, the reduced volume increases the local enzymatic activity, and on the other hand, collision with other molecules inhibits the diffusion of metabolites and enzymes. It already has been shown that this can either speed up or slow down the overall reaction [1]. Even though it is known that crowding affects the kinetics of enzymatic reactions, little is known about its effects on cellular metabolism. To investigate the effect of crowding on metabolic pathways we propose a molecular particle model for metabolic reactions based on Brownian Reaction Dynamics. Our model includes realistic crowding conditions to account for the diversity of macromolecules that do not participate in the observed reaction, also referred to as Crowders. We utilize this model to analyze the effects of crowding on enzymatic reactions systematically. Therefore, we parameterize kinetic models of enzymatic reactions that consider the elementary reaction steps. We use therefore physiological relevant concentrations, saturations, and thermodynamic displacements. To study the of effects crowding, we translate these models into particle models with equivalent kinetics. We further investigate the effects of crowding on the responses to changes in the enzyme concentrations of simple metabolic networks. In this study, we limit our investigation to simple linear and branched pathways. Our study shows that crowding affects enzyme kinetics under physiologically relevant conditions. We additionally present case studies on the consequences of crowding in metabolic pathways, revealing that further research is needed to understand the impact of crowding on metabolism. In the future, we plan to apply the developed framework to characterize crowding effects and develop new methods to account for them in large-scale kinetic models. [1] Ellis, R. J. Macromolecular crowding: obvious but underappreciated. Trends in Biochemical Sciences 26, 597-604, doi:10.1016/s0968-0004(01)01938-7 (2001).

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