Systems biology-based study of provitamin A carotenoid biosynthesis in Arabidopsis thaliana
Item
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Title
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Systems biology-based study of provitamin A carotenoid biosynthesis in Arabidopsis thaliana
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Identifier
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d_2009_2013:f1ef3de7c026:11118
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identifier
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11420
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Creator
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Tzfadia, Oren,
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Contributor
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Eleanore T. Wurtzel
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Date
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2011
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Language
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English
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Publisher
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City University of New York.
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Subject
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Plant sciences | Bioinformatics | Plant biology | Gene expression | Metabolic pathways | Microarray | Transcriptional regulation
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Abstract
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Due to their great nutritional and health value, understanding the regulatory mechanisms and recognizing new points of control in the carotenoid pathway can be the goal of breeding plans for increasing carotenoids accumulation in crop plants. Systems biology is an inter-disciplinary field, which integrates computational models and tools with molecular biology and different types of data including in silico transcriptomics, co-expression correlation, metabolomics, proteomics and phylogenetic information in order to develop hypotheses with statistically sound robustness. In the first steps of my work I describes the sequential use of freely available databases to explore the regulation of carotenoid biosynthesis in Arabidopsis during chloroplast development. The findings suggested that coordinated transcriptional regulation of genes along the isoprenoid-related biosynthesis pathways, play a major role in coordinating the synthesis of functionally related, chloroplast-localized isoprenoid-derived compounds. Next I aspired to find candidate genes that are participating in or regulating the carotenoid pathway. A model was developed to integrate several types of high-throughput data, in order to optimize candidate gene ranking in an effort to best define associated genes for a specific studied pathway. The candidate ranking was achieved by using an iterative algorithm (called MORPH), which is built on implementation of machine learning techniques. Application of the method on several biological pathways in Arabidopsis proved the ability of the algorithm to capture experimentally proven gene candidates related to known biological pathways. The robustness of the predictions provided by MORPH creates an exciting research methodology to explore regulation of biological pathways in plants. Although the development of the computational algorithm was initially triggered by the specific needs of our laboratory, namely, for close analysis of the carotenoid pathway, the algorithm is suitable for almost any biological pathway in plants. Moreover the method could be applied to any other model system that has enough available high-throughput data.
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Type
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dissertation
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Source
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2009_2013.csv
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degree
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Ph.D.
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Program
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Biology
