Kinetic coupling — breakthrough in understanding biochemical networks

Biochemical networks are the central processing units of a cell that enable it to process signals and convert molecules into building blocks that support cell functions. They are described by the structure and dynamics of the underlying chemical reactions in the cell.

These networks have been broken down into functional modules using the structure of the networks using bioinformatics methods. Kinetic modules are a type of functional modules that arise due to the interplay between network structure and dynamics. “We wanted to find out how kinetic modules in the biochemical networks determine the robustness of the concentrations of metabolites and what effects they have on the functionality of these networks,” explains Zoran Nikoloski, Professor for Bioinformatics at the University of Potsdam and a Cooperative Group Leader at the Max Planck Institute of Molecular Plant Physiology. Robustness refers to the ability of a network to maintain a constant concentration of metabolic products, across any change in the environment. This ensures the survival and growth of a cell in the event of various environmental fluctuations. The loss of robustness in the concentration of certain metabolites is considered a hallmark of many diseases.

Using a new concept of kinetic modules based on the kinetic coupling of reaction rates, the team analyzed 34 metabolic network models of 26 different organisms, including those of the model plant Arabidopsis thaliana, the model bacterium Escherichia coli, and the model fungus Saccharomyces cerevisiae. With their concept of kinetic modules, the researchers were able to successfully link the structure and dynamics of biochemical networks and thus clarify a systems biology question that has been open for three decades.

“Our results have wide implications for biotechnological and medical applications,” Nikoloski says. “We expect that the automated identification of modules can be used to deepen our understanding of the relationships between regulatory, signaling, and metabolic networks and of design principles that extend beyond network structure.”