Contextualizing Models: Deriving a Kinetic Model of Cancer Metabolism including Growth via Stoichiometric Reduction

Genome-scale metabolic models (GEMs) offer unprecedented possibilities to study human metabolism, including alterations in cancers. Yet, analyses of GEMs still entail several disadvantages. In particular, constraint-based methods, such as flux balance analysis, are typically restricted to analyse steady-state flux distributions. In contrast, kinetic models based on ordinary differential equations allow assessment of regulatory properties and dynamics. Building kinetic models, however, is still hampered by the lack of knowledge about kinetic parameters and is typically focused on individual pathways. Here, we present an approach to derive kinetic models of metabolism augmented by coarse-grained overall reactions that represent the remaining cellular metabolism and biosynthetic processes. Using algorithmic network reduction, we derive coarse-grained reactions that preserve the correct stoichiometry of precursors, energy, and redox equivalents required for cellular growth. Analysis of the GEM-embedded kinetic model uses Monte Carlo sampling to address parameter uncertainty. We exemplify our approach by constructing a kinetic model of cancer metabolism that includes an explicit description of cellular growth. We show that the GEM-embedded kinetic model differs in its control properties from the corresponding model without growth, with implications for understanding regulatory hotspots and drug target identification.