Great Lakes Bioenergy research consistently results in new discoveries and new technologies. Here, we highlight high-impact research from all three of our research areas.
GLBRC researchers have deciphered the biosynthesis of particular furan fatty acids in two species of α-proteobacteria by identifying the genes necessary and sufficient for the production of these furan fatty acids. Gas chromatography–mass spectrometry and nuclear magnetic resonance spectroscopy were used to identify the chemical structures of the products and intermediates of this pathway, and isotopic studies were conducted to determine the source of the oxygen atom in these furan fatty acids.
Ammonia fiber expansion (AFEX) is a pretreatment that uses heat and chemical energy to break down plant cell walls and allow enzymes easier access to convert the sugar polymers inside into sugars, which can be subsequently converted into fuels and chemicals. Scientists at the Great Lakes Bioenergy Research Center (GLBRC) have developed a standard operating procedure for performing AFEX pretreatment of bioenergy crops that can be safely applied at a lab-scale.
Over the course of eight years, researchers at the Great Lakes Bioenergy Research Center (GLBRC) assessed the carbon emissions associated with using various bioenergy crops for ethanol or electric-powered vehicles and compared them to petroleum.
Selecting the right solvent system to achieve product separation is often time-consuming and laborious. To alleviate that experimental burden, GLBRC researchers used computational methods to quickly and accurately identify optimal solvent systems for the liquid−liquid extraction of desired lignin monomers produced by four currently used depolymerization strategies.
Disease resistance in plants varies across populations and may be affected by changes in their environment. Researchers at the Great Lakes Bioenergy Research Center (GLBRC) studied one such disease in switchgrass—rust—to understand the genetic and environmental factors underlying the variation of switchgrass rust resistance.
Machine learning (ML) is a powerful tool for finding and analyzing relationships in large, complex biological datasets. ML works by identifying patterns in data and using those patterns to create models that can make predictions about new data. Although powerful, ML models cannot be easily understood by humans due to their complexity. Luckily, strategies to demystify the logic of ML models are available and continually improving.
GLBRC researchers developed an iterative method of Hybrid Production (iHyPr) in yeasts that allows the genomes of up to six different species to be combined into a single strain. Hybrids initially grew slowly but rapidly regained fitness and adapted, even as they retained traits from multiple species.
In this highly collaborative Great Lakes Bioenergy Research Center (GLBRC) study, researchers in plant biology, lignin chemistry, microbial fermentation, and technoeconomic modeling tackled the question of how to improve the economics of liquid fuel biorefineries.
In this collaborative paper, Great Lakes Bioenergy Research Center (GLBRC) scientists and colleagues at North Carolina State University produced triple-transgenic poplar with reduced expression of three lignin biosynthetic cytochrome P450 genes. Lignin from these transgenics had significantly higher levels of monolignol benzoates (ML-BA) as well as additional desirable traits, including reduced lignin content and improved saccharification efficiency.
In this paper, Great Lakes Bioenergy Research Center (GLBRC) scientists studied biorefinery strategies that couple lignin valorization subsystems with the conversion of biomass to liquid fuels, with a goal of exploring the advances and conditions necessary to make lignin valorization technically and economically viable