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Using New Computational Methods To Improve Biofuel Production
Published: May 19, 2011
Posted: July 15, 2011

Lignin gives plants their strength and helps make them resistant to diseases, but it also complicates the use of plant material for biofuel production because of its recalcitrance to deconstruction. Researchers have successfully manipulated the lignin biosynthetic pathway in biofuel-producing plant species; however, the modified plants often have unexplained or undesirable biological features. It is difficult to predict, given our current ability to model plant metabolic processes, how individual biosynthetic pathways connect together, influence each other, and are controlled. To address this challenge, Yun Lee and co-workers at DOE’s BioEnergy Research Center (BESC) have developed a new computational method that combines metabolic modeling with Monte Carlo (random sampling) simulations to enable the analysis of many biological pathways simultaneously. When this method was applied to the prediction of lignin biosynthesis in alfalfa, BESC researchers found that lignin generation was not due to a single process but involved many pathways. In addition, the researchers predicted, and later confirmed, that a possible control for lignin biosynthesis was the signaling molecule salicylic acid. This work addresses the complexity of plant biosynthetic pathways and provides a computational method that can help researchers decipher them, providing new tools that can be used to improve biofuel production.

Reference: Lee, Y., F. Chen, L. Gallego-Giraldo, R. A. Dixon, and E. O. Voit. 2011. "Integrative Analysis of Transgenic Alfalfa (Medicao sativa L.) Suggests New Metabolic Control Mechanisms for Monolignol Biosynthesis," PLoS Computational Biology 7(5), e1002047.

Contact: Susan Gregurick, SC-23.2, (301) 903-7672
Topic Areas:

  • Research Area: Plant Systems and Feedstocks, Plant-Microbe Interactions
  • Research Area: Sustainable Biofuels and Bioproducts
  • Research Area: DOE Bioenergy Research Centers (BRC)
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-33.2 Biological Systems Science Division, BER


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