A Genome-Scale Metabolic Model of Arabidopsis and Some of Its Properties    

Abstract

We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast.Historically, attempts to engineer plant metabolism for increased production of specific useful products have met with mixed success. Problems arise because of considerable metabolic redundancy that allows imposed genetic changes to be circumvented, because of an insufficiently detailed knowledge of the distribution of control of flux, and because the behavior of plant metabolism at the network level is not well described. While increasingly complex metabolic networks are now being characterized with steadystate stable isotope labeling experiments (Libourel and Shachar-Hill, 2008;Schwender, 2008;Kruger and Ratcliffe, 2009), the resulting flux maps still only cover a small percentage of the total metabolic network. The construction of a comprehensive plant metabolic model that includes the complete repertoire of catalyzed transformations represented within a specific genome (a genome-scale metabolic model) would represent a significant step forward in the development of a description of plant metabolic behavior at the network level.The aim of this work is to describe a genome-scale structural model of Arabidopsis (Arabidopsis thaliana) metabolism and to explore the utility of the model as a tool to characterize possible flux behavior states of the metabolic network. Arabidopsis is the logical choice for this exercise because of its well-annotated genome. Moreover, the translation of the Arabidopsis genome into a curated set of metabolic reactions is already well advanced, and the reaction lists are available through the Aracyc database (Mueller et al., 2003;Zhang et al., 2005). While this is an excellent starting point for metabolic modeling, uncritical use of such reaction lists is likely to generate models exhibiting a number of problems, the most fundamental of which is violation of mass conservation. Thus, considerable effort is required to generate a useful genome-scale metabolic model from these databases .Once a stoichiometrically balanced structural model is achieved, the investigator is then faced with the challenge o...

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

“…For example, in a recent computational study, Poolman et al (2009) reported a nonzero Rubisco oxygenase reaction as a result of a constraint-optimization problem in heterotrophic Arabidopsis cells. While the scenario discussed therein is probably unlikely to occur in vivo, as also acknowledged by the authors (Poolman et al, 2009;Stitt et al, 2010), the prediction of flux through previously unrecognized reactions as a result of network optimization is not unusual (Schwender et al, 2004). In this sense, a thorough network reconstruction allows researchers to evaluate the inevitable functional consequences, given the current annotation and knowledge of enzymatic interconversions, and thus opens the possibility of specifically designing experiments to distinguish between alternative hypotheses.…”

“…Specifically, FBA allows the prediction of optimal steady-state fluxes that maximize a given objective function, usually the synthesis of biomass or biomass precursors required for growth. Although certainly not without pitfalls, the predictions of FBA have proven to reasonably reflect the modes of cellular operation, with manifold applications ranging from microorganisms to algae and plant metabolism (Varma and Palsson, 1994;Shastri and Morgan 2005;Boyle andMorgan, 2009, Feist et al, 2009;Grafahrend-Belau et al, 2009;Oberhardt et al, 2009;Poolman et al, 2009).…”

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

“…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).…”

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

“…In this regard, metabolic network models for several plants, such as Arabidopsis (Arabidopsis thaliana; Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010a;Saha et al, 2011;Chung et al, 2012;Mintz-Oron et al, 2012), barley (Hordeum vulgare;Grafahrend-Belau et al, 2009), rapeseed (Brassica napus;Hay and Schwender, 2011;Pilalis et al, 2011), maize (Zea mays; Saha et al, 2011), and a general C 4 plant model (de Oliveira Dal'Molin et al, 2010b) have already been developed, and some of them are even in genome scale. Nevertheless, to the best of our knowledge, the metabolic model of rice is not available to date.…”