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PATTERN RECOGNITION IN THE DEVELOPING MAIZE LEAF EPIDERMIS: GENE NETWORK ANALYSES AND MACHINE LEARNING APPROACHES

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dc.contributor.authorQiao, Pengfei
dc.contributor.chairScanlon, Michael J.
dc.contributor.committeeMemberGore, Michael Allen
dc.contributor.committeeMemberSabuncu, Mert
dc.date.accessioned2019-10-15T16:47:53Z
dc.date.available2019-10-15T16:47:53Z
dc.date.issued2019-08-30
dc.description.abstractBiological systems contain data of high dimensions and magnitudes, including biochemistry, cellular patterning, transcriptomics, and genomics. Here I combined network analyses and machine learning, to identify developmental patterns that may be amenable to the improvement of drought tolerance. Cuticles comprise the hydrophobic lipid layer covering the aboveground plant body, and have long been a research focus into water conservation in plants. However, no prior studies have examined cuticle development across a temporal and spatial gradient in a crop plant. I used gene network analyses to correlate the biochemical/developmental gradient of cuticle components with the underlying transcriptomic transitions to identify the role of PHYTOCHROME B-mediated light signaling in cuticle development. Subsequent statistical and biochemical analysis revealed LIPID-TRANSFER PROTEINs as evolutionary novelties contributing to the emergence of cuticles in land plants. Additionally, combining the power of genome- and transcriptome-wide association studies (GWAS and TWAS), vesicular trafficking was implicated in the regulation of cuticular evaporation rate. Water loss through the leaf surface is also moderated by specialized cell types (bulliform cells) in maize. Bulliform cell ontogeny was investigated in the developing maize leaf, and a machine learning approach (convolutional neural networks) was employed to conduct high-throughput phenotyping of microscopic bulliform cell traits in 60,780 leaf epidermal glue-impression images. A subsequent GWAS analysis on bulliform cell column number and column width identified a set of gene candidates implicated to function in cell division and DNA methylation. Overall this dissertation demonstrates a multidisciplinary approach combining developmental biology, transcriptomics, quantitative genetics, machine learning, and statistical data analysis, toward a more holistic understanding of the mechanisms of water conservation in maize.
dc.identifier.doihttps://doi.org/10.7298/cvzs-s534
dc.identifier.otherQiao_cornellgrad_0058F_11633
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:11633
dc.identifier.otherbibid: 11050523
dc.identifier.urihttps://hdl.handle.net/1813/67540
dc.language.isoen_US
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBioinformatics
dc.subjectPlant sciences
dc.subjectComputer science
dc.titlePATTERN RECOGNITION IN THE DEVELOPING MAIZE LEAF EPIDERMIS: GENE NETWORK ANALYSES AND MACHINE LEARNING APPROACHES
dc.typedissertation or thesis
dcterms.licensehttps://hdl.handle.net/1813/59810
thesis.degree.disciplinePlant Biology
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh.D., Plant Biology

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