Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

TLDR

Plant metabolism spans cellular, tissue, and organ levels, and whole‑plant dynamics add further complexity through changing source‑sink relationships during development. The study aims to achieve a spatiotemporal resolution of source‑sink interactions in crop plant metabolism. A multiscale metabolic modeling (MMM) approach was used, integrating static organ‑specific models with a whole‑plant dynamic model and enabling dynamic flux balance analysis to decipher source and sink organ behavior during barley’s generative phase. The MMM analysis revealed a sink‑to‑source shift in the barley stem caused by leaf senescence reducing source capacity, and demonstrated that the platform provides a novel, systemic, spatiotemporally resolved tool for studying carbon partitioning, yield stability, and crop improvement.

Abstract

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.