Abstract
The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, total photosynthetic rate, storage root dry weight, and biomass function-related data. These mainly varied within ±90% of the model default values, although exceptions were made for the carbohydrate (−90% to 8%) and starch (−90% to 9%) contents. The results indicated that the predicted storage root growth rate was highly affected by specific sucrose uptake rates through the total photosynthetic rate and storage root dry weight variations; whereas the carbon flux distribution, direction and partitioning inclusive, was more sensitive to the variation in biomass content, particularly the carbohydrate content. This study showed that the specific sucrose uptake rate based on the total photosynthetic rate, storage root dry weight, and carbohydrate content were critical to the constraint-based metabolic modeling and deepened our understanding of the input–output relationship—specifically regarding the rMeCBM-KU50 model—providing a valuable platform for the modeling of plant metabolic systems, especially long-growing crops.
Highlights
The complexity of plant metabolism hinders experimental studies to unravel the fate of metabolic substrates derivation for biomass production, and it is even harder to unwind the underlying regulations.As multicellular organisms, plants are composed of cells with several subcellular compartments, defined as organelles; as a result, metabolic processes are fragmented into parts that are exposed to heterogeneous surrounding environments [1]
The rMeCBM-KU50 model was evaluated according to the sensitivity of predicted results, based upon (i) substrate–uptake related data, and (ii) biomass function-related data
The specific sucrose uptake rate employed in the model was calculated from experimentally determined data on the photosynthetic rate, total leaf area, and storage root dry weight of cassava
Summary
The complexity of plant metabolism hinders experimental studies to unravel the fate of metabolic substrates derivation for biomass production, and it is even harder to unwind the underlying regulations.As multicellular organisms, plants are composed of cells with several subcellular compartments, defined as organelles; as a result, metabolic processes are fragmented into parts that are exposed to heterogeneous surrounding environments [1]. The complexity of plant metabolism hinders experimental studies to unravel the fate of metabolic substrates derivation for biomass production, and it is even harder to unwind the underlying regulations. A constraint-based modeling (CBM) approach with flux balance analysis (FBA) was employed to deepen the understanding of plant metabolism through simulation. The CBM approach enables the study of biological systems and characterization of metabolic network behavior by flux analysis under steady-state assumptions, integrating biochemical, genetic, and genomic information [2]. Constraints and the biomass objective function, which are the heart of the CBM approach for identifying biologically relevant flux solutions, were established based on organism-specific and/or condition-specific experimental measurements [3,4,5,6,7]. In case of Processes 2019, 7, 259; doi:10.3390/pr7050259 www.mdpi.com/journal/processes
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