Abstract
The use of large-scale or genome-scale metabolic reconstructions for modeling and simulation of plant metabolism and integration of those models with large-scale omics and experimental flux data is becoming increasingly important in plant metabolic research. Here we report an updated version of bna572, a bottom-up reconstruction of oilseed rape (Brassica napus L.; Brassicaceae) developing seeds with emphasis on representation of biomass-component biosynthesis. New features include additional seed-relevant pathways for isoprenoid, sterol, phenylpropanoid, flavonoid, and choline biosynthesis. Being now based on standardized data formats and procedures for model reconstruction, bna572+ is available as a COBRA-compliant Systems Biology Markup Language (SBML) model and conforms to the Minimum Information Requested in the Annotation of Biochemical Models (MIRIAM) standards for annotation of external data resources. Bna572+ contains 966 genes, 671 reactions, and 666 metabolites distributed among 11 subcellular compartments. It is referenced to the Arabidopsis thaliana genome, with gene-protein-reaction (GPR) associations resolving subcellular localization. Detailed mass and charge balancing and confidence scoring were applied to all reactions. Using B. napus seed specific transcriptome data, expression was verified for 78% of bna572+ genes and 97% of reactions. Alongside bna572+ we also present a revised carbon centric model for 13C-Metabolic Flux Analysis (13C-MFA) with all its reactions being referenced to bna572+ based on linear projections. By integration of flux ratio constraints obtained from 13C-MFA and by elimination of infinite flux bounds around thermodynamically infeasible loops based on COBRA loopless methods, we demonstrate improvements in predictive power of Flux Variability Analysis (FVA). Using this combined approach we characterize the difference in metabolic flux of developing seeds of two B. napus genotypes contrasting in starch and oil content.
Highlights
Large-scale metabolic network reconstruction, modeling, and simulation is becoming increasingly important in plant systems biology and metabolic engineering (Schwender, 2011; Sweetlove and Ratcliffe, 2011; Collakova et al, 2012; Seaver et al, 2012; De Oliveira Dal’molin and Nielsen, 2013; Sweetlove et al, 2014)
Using two genotype accessions of B. napus that differ in seed composition (Table 1), we demonstrate integration of flux information obtained from 13C-Metabolic Flux Analysis (Table 2) with loopless Flux Variability Analysis (FVA)
In an accompanying paper (Schwender et al, 2014) we explore how, using the Arabidopsis genome as a proxy, B. napus hiseq transcriptome sequencing data can be integrated with bna572+
Summary
Large-scale metabolic network reconstruction, modeling, and simulation is becoming increasingly important in plant systems biology and metabolic engineering (Schwender, 2011; Sweetlove and Ratcliffe, 2011; Collakova et al, 2012; Seaver et al, 2012; De Oliveira Dal’molin and Nielsen, 2013; Sweetlove et al, 2014). Draft models have been generated for seven additional plants with sequenced genomes (Seaver et al, 2014). The methods framework being generally used has been summarized as COnstraint-Based Reconstruction and Analysis (COBRA) (Thiele and Palsson, 2010). A metabolic reconstruction of an organism can be understood as a highly structured representation of biochemical, genomic, and physiological information which allows derivation of a www.frontiersin.org In constraint-based (CB) analysis, networks are simulated using mathematical constraints related to mass conservation (reaction stoichiometry), thermodynamics (reaction directionality) and various physiological/experimental data.
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