Energy metabolism controls phenotypes by protein efficiency and allocation.

Cells rely on energy metabolism to support growth and function, yet the coordination of energy production with biomass formation remains unclear. While it is known that organisms use multiple metabolic pathways to generate ATP, the interplay between these pathways and the allocation of proteins to them is not fully understood. Prior research has shown that energy pathways often overlap with those used for biomass synthesis, complicating analysis. This overlap makes it difficult to isolate the role of each pathway in energy production. Existing models struggle to account for how protein resources are distributed among competing metabolic functions. The challenge lies in separating energy production from biomass formation, which are tightly coupled in cellular systems. Limited data on protein allocation across conditions has hindered progress in this area. Understanding how cells balance these processes could reveal strategies to enhance growth or productivity. This gap motivated the development of a new modeling framework to dissect energy metabolism into distinct components.

The study aimed to clarify how energy metabolism and protein allocation interact to determine cellular phenotypes. The specific problem addressed is the difficulty of separating energy production from biomass formation in metabolic models. The motivation stems from the need to understand how cells optimize energy use under different conditions. By decomposing energy metabolism into distinct pathways, the researchers sought to estimate protein efficiency for each. This approach allows for a clearer analysis of how protein resources are distributed. The study focuses on two well-characterized organisms, Escherichia coli and Saccharomyces cerevisiae, to test the model's applicability. The goal is to determine whether energy metabolism alone can explain metabolic switches like overflow metabolism and the Crabtree effect. This work provides a framework to explore how protein constraints influence metabolic behavior.

The researchers developed a modeling concept that separates energy metabolism into biomass formation and ATP-producing pathways. The ATP-producing pathways were further divided into high-yield and low-yield systems. This decomposition allowed for independent estimation of protein efficiency in each pathway. The model was applied to Escherichia coli and Saccharomyces cerevisiae using available proteomics data. Protein efficiency was calculated based on the amount of protein allocated per unit of ATP produced. Absolute proteomics measurements were used to assess protein mass conservation across conditions. The model predicted overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae. These predictions were tested against known metabolic behaviors to validate the approach.

The model revealed that the high-yield ATP pathway in both organisms has lower protein efficiency than the low-yield pathway. This finding suggests a trade-off between ATP yield and protein usage. Protein mass in energy metabolism remained constant across different conditions. This conservation supports the idea of a static protein constraint. The model predicted overflow metabolism in E. coli when glucose uptake is high. Similarly, the Crabtree effect in S. cerevisiae was explained by energy metabolism alone. Enzyme activity may decrease at low glucose uptake rates, affecting pathway efficiency. These results indicate that energy metabolism can account for metabolic switches without additional regulatory mechanisms.

The study concludes that energy metabolism can explain metabolic switches in E. coli and S. cerevisiae. The model supports the idea that protein allocation is constrained and conserved across conditions. Lower protein efficiency in high-yield pathways suggests a limitation in maximizing ATP production. The static protein constraint is supported by proteomics data showing constant mass. The findings imply that enzyme activity may decrease at low glucose uptake rates. The model identifies three potential strategies to improve growth: increasing protein allocation to energy metabolism, reducing ATP demand, or enhancing key enzyme activity. These conclusions align with the authors' predictions based on the model. The study does not propose new regulatory mechanisms but highlights the role of protein constraints in metabolic behavior.