Responses to Light Intensity in a Genome-Scale Model of Rice Metabolism      

Abstract

We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms.

Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multi-omics analysis

“…Clearly, it is more extensive than the previous one (Poolman et al, 2013) in terms of metabolic pathways coverage, appropriate subcellular localization of reactions, detailed accounting of electron transport metabolism in both plastid and mitochondrion, enhanced network connectivity, and a low percentage of blocked reactions (for thorough comparisons, see Supplemental Fig. S3 and Supplemental Data Set S1).…”

“…Moreover, these models can contextualize multiple omics data through several integrative analyses, as such providing unique biological insights at the systems level (Hyduke et al, 2013). Several constraints-based models have been developed at the genome scale for a wide range of microbes and mammals (Kim et al, 2012b), including humans (Duarte et al, 2007), and a few plants, such as Arabidopsis (Poolman et al, 2009;Saha et al, 2011;MintzOron et al, 2012;Chung et al, 2013), maize (Saha et al, 2011;Simons et al, 2014), and rice (Poolman et al, 2013). Among these, the human genome-scale metabolic model (GEM) has been integrated with transcriptome and proteome data to characterize the transcriptional regulatory mechanisms and metabolic phenotypes of various diseases, which could not be deciphered from either of them alone (Zelezniak et al, 2010;Hu et al, 2013;Mardinoglu et al, 2014).…”

Bioenergetics of Monoterpenoid Essential Oil Biosynthesis in Nonphotosynthetic Glandular Trichomes

“…One promising approach involves modeling metabolism more globally. Genome-scale stoichiometric models have been produced for various model plant systems, including leaf cells of Arabidopsis (Arabidopsis thaliana; Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010a;Mintz-Oron et al, 2012;Cheung et al, 2013;Arnold and Nikoloski, 2014), maize (Zea mays; de Oliveira Dal 'Molin et al, 2010b;Saha et al, 2011), rice (Oryza sativa; Poolman et al, 2013), and embryos of rapeseed (Brassica napus; Hay and Schwender, 2011). Building on these preexisting models, we developed a stoichiometric model reflecting metabolism in mint GTs by integrating newer transcriptome data sets with the wealth of knowledge on this subject in the published literature.…”

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

“…To build this systems-level understanding, several genomescale metabolic reconstructions have recently been published for plant species (Poolman et al, 2009;de Oliveira Dal'molin et al, 2010a,b;Saha et al, 2011;Poolman et al, 2013). Each reconstruction consists of all reactions known to be catalyzed by one or more of the gene products in the plant genome.…”