Thermodynamically Explicit Kinetics with Potential for Genome-wide Application

Metabolic processes are fundamental to life, and understanding their dynamic behavior is crucial for addressing biological questions and advancing applications in medicine and biotechnology. While genome-scale metabolic network reconstructions and constraint-based modeling, such as Flux Balance Analysis (FBA), have provided valuable insights into steady-state metabolism, their inherent limitations in capturing dynamic behavior restrict our ability to predict and manipulate metabolic responses, particularly in complex scenarios like disease. This is especially critical for understanding and treating conditions like cancer. This work addresses this challenge by developing a thermodynamically explicit (TDE) parametrization of kinetic models for metabolic reaction systems, grounded in the principles of equilibrium and non-equilibrium thermodynamics. Specifically, we (1) revisit classical kinetic modeling approaches using Michaelis-Menten and convenience kinetics, (2) leverage thermodynamic principles to derive a TDE parametrization method, and (3) demonstrate the impact of TDE parametrization by comparing simulations of a realistic metabolic model using both classical and TDE approaches. We anticipate that this TDE kinetic approach will be a valuable tool for genome-scale parametrization of dynamic metabolic network models, enabling more accurate and predictive simulations of metabolic behavior under diverse conditions.