(620h) Introducing an Optimization- and Explicit Runge-Kutta- Based Approach to Perform Dynamic Flux Balance Analysis

Genome-scale Models (GSMs) of metabolism have become important tools for the silico study and design of metabolism in silico. As GSMs are under-defined systems of equations, optimization-based tools are required for their analysis, the most common of which is Flux Balance Analysis (FBA). A perturbation of FBA, dynamic FBA (dFBA), is a tool which allows for the study of a modeled system across time, including biomass and metabolite concentration. Introduced here is a generalized Optimization- and explicit Runge-Kutta-based Approach (ORKA) to perform dFBA, which is more accurate and computationally tractable than existing approaches, namely the Static Optimization Approach (SOA) and the Dynamic Optimization Approach (DOA). ORKA is applied to a four-tissue (leaf, root, seed, and stem) model of Arabidopsis thaliana, p-ath773, which uniquely captures the core-metabolism of several stages of growth from seedling to senescence while strongly emphasizing plant-scale behavioral agreement between in silico results and in vivo data. Using ORKA, p-ath773 takes metabolic “snapshots” at hourly intervals throughout the lifecycle of an individual plant. It next shows the transition of metabolism and whole-plant growth, such as the evolution 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 shows frequent cross-compartment exchange of a standing pool of amino acids which is used to regulate proton flow. In addition, p-ath773 has shown broad agreement with published plant-scale properties such as mass, maintenance, and senescence. Overall, p-ath773 serves as a scaffold for lifecycle models of other plants to further increase the range of hypotheses which can be investigated in silico.

W. L. Schroeder and R. Saha. “Introducing and Optimization- and Explicit Runge-Kutta- based Approach to Perform Dynamic Flux Balance Analysis”. BioRxiv, Apr. 29, 2020. Available: https://www.biorxiv.org/content/10.1101/761189v2 (doi: https://doi.org/10.1101/761189)