Developing embryos after being excised from a growing rapeseed plant. The embryos accumulate seed oils which represent the most energy-dense form of biologically stored sunlight, and have great potential as renewable resources for fuel and industrial chemicals. Image: Brookhaven National Laboratory |
Scientists at the U.S. Department of Energy’s (DOE) Brookhaven
National Laboratory have developed a computational model for analyzing the
metabolic processes in rapeseed plants—particularly those related to the
production of oils in their seeds. Their goal is 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 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,” says Brookhaven biologist Jorg
Schwender, who led the development of the model with postdoctoral research
associate Jordan Hay.
In the case of plant oils, the scientists’ attention is 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 says.
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 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.
“It’s like trying to assess traffic flow on roads in the United States
by measuring traffic flow only on the major highways,” Schwender says.
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.
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.
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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 says, “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.
“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,” Schwender
says.
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,” Schwender says. “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.”
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.
