Flux sampling is a powerful tool to study metabolism under changing environmental conditions - Herrmann, Helena A.; Dyson, Beth C.; Vass, Lucy; Johnson, Giles N.; Schwartz, Jean-Marc (NPJ systems biology and applications, 2019)
The development of high-throughput ‘omic techniques has sparked a rising interest in genome-scale metabolic models, with applications ranging from disease diagnostics to crop adaptation. Efficient and accurate methods are required to analyze large metabolic networks. Flux sampling can be used to explore the feasible flux solutions in metabolic networks by generating probability distributions of steady-state reaction fluxes. Unlike other methods, flux sampling can be used without assuming a particular cellular objective. We have undertaken a rigorous comparison of several sampling algorithms and concluded that the coordinate hit-and-run with rounding (CHRR) algorithm is the most efficient based on both run-time and multiple convergence diagnostics. We demonstrate the power of CHRR by using it to study the metabolic changes that underlie photosynthetic acclimation to cold of Arabidopsis thaliana plant leaves. In combination with experimental measurements, we show how the regulated interplay between diurnal starch and organic acid accumulation defines the plant acclimation process. We confirm fumarate accumulation as a requirement for cold acclimation and further predict γ –aminobutyric acid to have a key role in metabolic signaling under cold conditions. These results demonstrate how flux sampling can be used to analyze the feasible flux solutions across changing environmental conditions, whereas eliminating the need to make assumptions which introduce observer bias. Metabolic modeling: flux sampling to study acclimation Flux sampling minimizes observer bias and is therefore a powerful tool for studying metabolic acclimation. Unlike other methods, flux sampling can be used without assuming that metabolism is optimized towards a single objective. This is particularly useful when studying metabolism in changing environments. A team lead by Jean-Marc Schwartz compared three different flux sampling algorithms in order to identify the most appropriate method to analyze changes in metabolic phenotypes. They applied flux sampling to several large-scale networks of plant metabolism to predict metabolic properties across changing temperatures. In order to breed plants for global climate changes, the limits on photosynthesis and metabolism across different environmental conditions need to be understood. Here, they used flux sampling methods to understand changes in photosynthesis and carbon accumulation in cold temperatures. They showed how an intricate balance between carbon and nitrogen metabolism is crucial for acclimation to cold.