Complex Networks Identify Genes for Biofuel Crops
(U.S. Department of Energy/Phys.Org) To improve biofuel production, scientists must understand the fundamental interactions that lead to the expression of key traits in plants and microbes. To understand these interactions, scientists are using different layers of information (about the relationships between genes, and between genes and phenotypes) combined with new computational approaches to integrate vast amounts of data in a modeling framework. Researchers can now identify genes controlling important traits to target biofuel and bioproduct production. The algorithm used in this work has been used to break the supercomputing exascale barrier for the first time anywhere in the world.
This approach lets scientists analyze massive data sets. They can do so using exascale computing, where computers perform 1018 calculations per second. With this approach, scientists can understand how cells work. They can use the insights to bioengineer beneficial traits into plants and microbes. The ability to use exascale computing opens up possibilities to study highly complex and interrelated molecular processes in cells at a level of detail not previously possible. Such computing also heralds a new era for systems biology.
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To better understand the molecular interactions involved in recalcitrance and identify target genes involved in lignin biosynthesis/degradation, this study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomics data (the concentrations of the metabolites) and pyrolysis-molecular beam mass spectrometry data. In addition, the scientists used other forms of gene regulation including co-expression, co-methylation, and co-evolution networks. READ MORE Abstract (Frontiers in Energy Research)
