Scientists at the US Department of Energy’s (DOE) Brookhaven National Laboratory (BNL) have developed a computational model for analyzing the metabolic processes in rapeseed plants, particularly those related to the production of oils in their seeds. The goal is to find ways to optimize the production of plant oils that have potential as renewable resources for fuel and industrial chemicals.
The model, described in two featured articles in the 1 August 2011 issue of The Plant Journal may help to identify ways to maximize the conversion of carbon to biomass to improve the production of plant-derived biofuels.
To make efficient use of all that plants have to offer in terms of alternative energy, replacing petrochemicals in industrial processes, and even nutrition, it’s essential that we understand their metabolic processes and the factors that influence their composition. [Oil in plant seeds] represents the most energy-dense form of biologically stored sunlight, and its production is controlled, in part, by the metabolic processes within developing seeds.—Brookhaven biologist Jörg Schwender
|A network illustrating some of the reactions and chemical pathways involved in oil production in rapeseed plants. Source: BNL. Click to enlarge.|
One way to study these metabolic pathways is to track the uptake and allotment of carbon-13 (13C) as it is incorporated into plant oil precursors and the oils themselves. But this method has limits in the analysis of large-scale metabolic networks such as those involved in apportioning nutrients under variable physiological conditions.
To address these more complex situations, the Brookhaven team constructed a computational model of a large-scale metabolic network of developing rapeseed (Brassica napus) embryos, based on information mined from biochemical literature, databases, and prior experimental results that set limits on certain variables.
The model includes 572 biochemical reactions that play a role in the seed’s central metabolism and/or seed oil production, and incorporates information on how those reactions are grouped together and interact.
Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types.
Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10–11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.—Hay and Schwender 2011a
The scientists first tested the validity of the model by comparing it to experimental results from carbon-tracing studies for a relatively simple reaction network—the big-picture view of the metabolic pathways. At that big-picture level, results from the two methods were largely consistent, providing validation for both the computer model and the experimental technique, while identifying a few exceptions that merit further exploration.
The scientists then used the model to simulate more complicated metabolic processes under varying conditions—for example, changes in oil production or the formation of oil precursors in response to changes in available nutrients (such as different sources of carbon and nitrogen), light conditions, and other variables.
The model also allows the researchers to assess the potential effects of genetic modifications (for example, inactivating particular genes that play a role in plant metabolism) in a simulated environment. These simulated knock-out experiments gave detailed insights into the potential function of alternative metabolic pathways—for example, those leading to the formation of precursors to plant oils, and those related to how plants respond to different sources of nitrogen.
The model has helped us construct a fairly comprehensive overview of the many possible alternative routes involved in oil formation in rapeseed, and categorize particular reactions and pathways according to the efficiency by which the organism converts sugars into oils. So at this stage, we can enumerate, better than before, which genes and reactions are necessary for oil formation, and which make oil production most effective.—Jörg Schwender
The researchers emphasize that experimentation will still be essential to further elucidating the factors that can improve plant oil production.
Any kind of model is a largely simplified representation of processes that occur in a living plant. But it provides a way to rapidly assess the relative importance of multiple variables and further refine experimental studies. In fact, we see our model and experimental methods such as carbon tracing as complementary ways to improve our understanding of plants’ metabolic pathways.—Jörg Schwender
The scientists are already incorporating information from this study that will further refine the model to increase its predictive power, as well as ways to extend and adapt it for use in studying other plant systems.
This work was supported by the DOE Office of Science.
Hay, J. and Schwender, J. (2011a), Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos. The Plant Journal, 67: 526–541. doi: 10.1111/j.1365-313X.2011.04613.x
Hay, J. and Schwender, J. (2011b), Computational analysis of storage synthesis in developing Brassica napus L. (oilseed rape) embryos: flux variability analysis in relation to 13C metabolic flux analysis. The Plant Journal, 67: 513–525. doi: 10.1111/j.1365-313X.2011.04611.x