Unlocking the full potential of kinetic metabolic models through characterization and reduction of uncertainty

Kinetic models are the most promising tool for comprehending the complex dynamic behavior of living cells. The primary goal of kinetic models is to capture the properties of the metabolic networks as a whole, and we need large-scale models for dependable in silico analyses of metabolism. However, the uncertainty in kinetic parameters is the main obstacle hindering the development of kinetic models, and the levels of uncertainty increase with the size of the models. Therefore, there is a need for tools that will address the issues with the uncertainty in parameters, and that will be able to reduce the uncertainty propagation through the system. In this work, we employed a method called iSCHRUNK that combines parameter sampling and parameter classification techniques to characterize the uncertainties and to uncover intricate relationships between the parameters of kinetic models and the responses of the metabolic network. The proposed method allowed us to identify only a few parameters that determine the responses in the network regardless of the values of other parameters. As a consequence, in future studies of metabolism, it will be sufficient to explore a reduced kinetic space, and more comprehensive analyses of large-scale and genome-scale metabolic networks will be computationally tractable.