Exophiala dermatitidis can be a model polyextremotolerant and melaninized organismIts genome-scale model is reconstructed to study melanin and carotenoid metabolismThe shadow price analysis indicates a potential underexplored role of carotenoids Comparisons between human and E. dermatitidis melanin synthesis are made

Stoichiometric metabolic modeling, particularly genome-scale models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions but cannot avoid thermodynamically infeasible cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill, which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle-free gapfilling solutions. In addition, OptFill can be adapted to automate inherent TICs identification in any GSM. Overall, OptFill can address critical issues in automated development of high-quality GSMs.

Article Length: 9539 words (excluding abstract, summary, references, captions) 1 8 Abstract 2 2Stoichiometric Models of metabolism have proven valuable tools for increased understanding of 2 3 metabolism and accuracy of synthetic biology interventions to achieve desirable phenotypes. 4Such models have been used in conjunction with optimization-based and have provided 2 5 "snapshot" views of organism metabolism at specific stages of growth, generally at exponential 2 6 growth. This approach has limitations in that metabolic history of the modeled system cannot be 2 7 studied. The inability to study the complete metabolic history has limited stoichiometric 2 8 metabolic modeling only to the static investigations of an inherently dynamic process. In this 2 9 work, we have sought to address this limitation by introducing an optimization-based 3 0 computational framework and applying to a stoichiometric model of the model plant Arabidopsis 3 1 thaliana of four linked sub-models of leaf, root, seed, and stem tissues which models the core 3 2 carbon metabolism through the lifecycle of arabidopsis (named as p-ath780). Uniquely, this 3 3 framework and model considers diurnal metabolism, changes in tissue mass, carbohydrate 3 4 storage, and loss of plant mass to senescence and seed dispersal. p-ath780 provide "snapshots" of 3 5 core-carbon metabolism at one hour intervals of growth, in order to show the evolution of 3 6 metabolism and whole-plant growth across the lifecycle of a single representative plant. Further, 3 7 it can simulate important growth stages including seed germination, leaf development, flower 3 8 production, and silique ripening. The computational framework has shown broad agreement with 3 9 published experimental data in tissue mass yield, maintenance cost, senescence cost, and whole-4 0 plant growth checkpoints. Having focused on core-carbon metabolism, it serves as a scaffold for 4 1 lifecycle models of other plant systems, to further increase the sophistication of in silico 4 2 metabolic modeling, and to increase the range of hypotheses which can be investigated in silico.4 3 A Lifecycle Metabolic Modeling Framework Schroeder & Saha, 2019 3 As an example, we have investigated the effect of alternate growth objectives on this plant over 4 4 the lifecycle. 4 5 4 6 Author Summary 4 7In an attempt to study the evolution of metabolism across the lifecycle of plants, in this work we 4 8 have created an optimization-based framework for the in silico modeling of plant metabolism 4 9across the lifecycle of a model plant. We then applied this framework to four core-carbon tissue-5 0 level (namely, leaf, root, seed, and stem) stoichiometric models of the model plant species 5 1 Arabidopsis thaliana, and further informed this framework with a wide array of published in vivo 5 2 data to increase model and framework accuracy. Unique to the p-ath780 model, comparted to 5 3 other models of plant metabolism, is the simultaneous considerations of diurnal metabolism, 5 4 carbohydrate storage, changes in tissue mass (in...

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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.

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N +) and nitrogen-deficient (N -) conditions. Model-predicted flux-sum variability analysis achieved 70% accuracy comparing to the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N -. Overall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and engineer better stress-tolerant maize genotypes.

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N−) and nitrogen-deficient (N−) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. Overall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes.SummaryThe growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction.A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc.Transcriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N−.Overall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes.

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