BNL develops plant modeling system to optimize oil production

By Bryan Sims | August 10, 2011

Researchers at the U.S. DOE’s Brookhaven National Laboratory have devised a computational model for analyzing the metabolic processes in rapeseed plants, particularly those related to the production of oils in their seeds. The goal was to find ways to optimize the production of plant oils that have widespread potential as renewable resources for fuel and industrial chemicals.

The model, described in two featured articles in the August issue of the Plant Journal, may help identify ways to maximize the conversion of carbon to biomass to improve the production of plant-derived biofuels, according to Jorg Schwender, BNL biologist who led the development of the model with postdoctoral research associate Jordan Hay.

“This computational model is based on simply taking the structural matrixes of biochemical reactions, collecting them together into a network of reactions,” Schwender told Biodiesel Magazine. “In other words, it’s based on a network of metabolites that are connected by reactions and each metabolite is accounted by its steady state. We basically make a net mass balance over the network.”

In the case of plant oils, their attention was focused on seeds where oils are formed and accumulated during development.

“This oil represents the most energy-dense form of biologically stored sunlight and its production is controlled, in part, by the metabolic processes within developing seeds,” Schwender said.

One way to study these metabolic pathways is to track the uptake and allotment of a form of carbon known as carbon-13 as it’s incorporated into plant oil precursors and the oil themselves, Schwender explained, but this method has limits in the analysis of large-scale metabolic networks such as those involved in apportioning nutrients under variable physiological conditions.

Schwender explained how this process is analogous to assessing traffic flow on roads in the U.S. by measuring traffic flow only on major highways.

“This kind of network construction and simulation has its advantages because you can look at pretty large networks with hundreds of reactions, since you don’t need much more information than the reaction steps of each particular reaction,” Schwender said.

To address these more complex situations, the scientists constructed a computational model of a large-scale metabolic network of developing rapeseed embryos—specifically Brassica napus—based on information gathered 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.

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 analogous to the traffic on U.S. highways. 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.

“This large-scale model is a much more realistic network, like a map that represents almost every street,” Schwender said, “with computational simulations to predict what’s going on.”

Continuing the traffic analogy, he said, “We can now try to simulate the effect of ‘road blocks’ or where to add new roads to most effectively eliminate traffic congestion.”

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.

Based on biochemical literature in attempts that manipulate content by transgenic approaches in plants like Brassica, Schwender said he has typically got no better than a 10 percent shift in the oil content.

“But, if you look across different crop plants that are available, there are quite a bit of oil contents across different types of species with different types of oil-storing seeds,” he said, “so the idea is that, in principle, there must be potential there to produce much more oil, but there is some kind of basic resistance level of the metabolic network that somehow resists this kind of manipulation to get more oil.”

Schwender and Hay 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.

“We plan to take this network more or less as a basic network construction that can be adapted to other species if we can get information about the enzyme expression in those particular species,” Schwender said.


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