Abstract
In this work we introduce the generalized Optimization- and explicit Runge-Kutta-based Approach (ORKA) to perform dynamic Flux Balance Analysis (dFBA), which is numerically more accurate and computationally tractable than existing approaches. ORKA is applied to a four-tissue (leaf, root, seed, and stem) model of Arabidopsis thaliana, p-ath773, uniquely capturing the core-metabolism of several stages of growth from seedling to senescence at hourly intervals. Model p-ath773 has been designed to show broad agreement with published plant-scale properties such as mass, maintenance, and senescence, yet leaving reaction-level behavior unconstrainted. Hence, it serves as a framework to study the reaction-level behavior necessary for observed plant-scale behavior. Two such case studies of reaction-level behavior include the lifecycle progression of sulfur metabolism and the diurnal flow of water throughout the plant. Specifically, p-ath773 shows how transpiration drives water flow through the plant and how water produced by leaf tissue metabolism may contribute significantly to transpired water. Investigation of sulfur metabolism elucidates frequent cross-compartment exchange of a standing pool of amino acids which is used to regulate the proton flow. Overall, p-ath773 and ORKA serve as scaffolds for dFBA-based lifecycle modeling of plants and other systems to further broaden the scope of in silico metabolic investigation.
Highlights
The use of synthetic biology for the engineering of uni- and multi-cellular organisms to enhance desirable phenotypes in microbe, plant, and animal systems, has been well established and has been capable of affecting the lives of millions of individuals, such as in the case of artemisinin production in yeast or enhancing nutritional value of agricultural products[1,2]
When Optimization- and explicit Runge-Kutta –based Approach (ORKA) has been applied to the p-ath[773] multi-tissue model, the order of error of both mass step and metabolite concentration estimates has been theoretically improved by approximately three order of magnitude as compared to that achieved in a previous model of Arabidopsis which utilized the Static Optimization-based Approach (SOA) to perform dFBA19
The basis of the ORKA is the same as SOA, to model a dynamic metabolism across multiple time points, where each time point solution builds upon previous solutions
Summary
The use of synthetic biology for the engineering of uni- and multi-cellular organisms to enhance desirable phenotypes in microbe, plant, and animal systems, has been well established and has been capable of affecting the lives of millions of individuals, such as in the case of artemisinin production in yeast or enhancing nutritional value of agricultural products[1,2]. We have developed a core-carbon metabolic model of Arabidopsis, named p-ath[773] (plant-scale core-metabolism Arabidopsis thaliana model with 773 genes included), to model the full lifecycle of Arabidopsis from germination to senescence by being embedded in the ORKA framework which captures metabolic interactions between four major tissues: leaf, root, seed, and stem. It should be noted that for metabolic models with a single tissue, or single organism, O(h3) or better error order is possible depending on the Runge-Kutta method selected, compared to the O(h2) error order floor of the SOA method This low error level has proved impossible to achieve with the p-ath[773] model since the seed tissue appears and disappears over the course of the Arabidopsis lifecycle, causing difficulties due to the exponential nature of FBA-determined growth rates. The p-ath[773] model embedded in the ORKA framework has shown general agreement with macro-level experimental data found in the literature and is potentially useful as steppingstone for dynamic lifecycle modeling of other plant systems
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