Project 8. Experimental and Computational study on biofuel production using fungi. (Sunil Shende, Computer Science, Kwangwon Lee, Biology)
Background. Producing a sustainable cellulosic biofuel production is one of the national priorities. In US, the main feedstock of biofuel production has been corn. There is a need to develop alternative feedstock for biofuel production, and economically viable technology. Our lab is working on a project in producing cellulosic biofuel using grasses by fungi.
Research. In previous study in our lab, we have identified natural variation of cellulase production and fermentation among ecotypes of the fungus Neurospora crassa. Our next step is to identify genes that are crucial responsible for the variation of the genes. Most inheritable phenotypes are multigenic traits. Identifying the multiple genetic loci that contribute to a complex phenotype and characterizing the interactions between the QTL (Quantitative Trait Loci) is an experimentally and computationally challenging task. Recent advances in statistical analysis tools, increased computational power, and full genome sequences yield the potential to characterize complex biological phenomena at both genomic and molecular levels.
Student activities. An REU student will 1) measure the variation of cellulase/ethanol production in 70 different ecotypes of N. crassa using grass as a substrate, 2) learn the program language R to perform QTL analysis using the similar data set we have generated, 3) perform QTL analysis using the data set in generated by the student.