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Dissertation
Development of Computational Models, Biomarkers, and Tools for Postharvest Traits in Malus Domestica Fruit
Washington State University
Doctor of Philosophy (PhD), Washington State University
2023
DOI:
https://doi.org/10.7273/000006299
Abstract
Understanding Malus domestica (apple) postharvest biology under typical storageconditions is important for ensuring that a high-quality product reaches consumers and for food waste reduction. Despite this, minimal research has been performed investigating the molecular mechanisms at work during storage. The following dissertation uses transcriptomic data and modeling techniques to investigate this biology. Chapter one is a brief literature review on modeling techniques and apple postharvest biology. Chapter two investigates how the core apple hypoxia response differs from other plants and how different postharvest treatments impact apple biology over long-term storage. Chapter three investigates how we can use machine learning models to develop transcriptomic biomarkers for predicting phenotypic traits in apples. Chapter four investigates currently open questions about data quantity and normalization techniques for modeling transcriptomic traits. Finally, chapter 5 reflects on the lessons learned from this research and on my experiences as a Ph.D. student. This research uncovers potential neo-functionalizations of genes, transcriptomic biomarkers, and a better understanding of modeling using transcriptomic data.
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Details
- Title
- Development of Computational Models, Biomarkers, and Tools for Postharvest Traits in Malus Domestica Fruit
- Creators
- John Anthony Hadish
- Contributors
- Stephen Ficklin (Advisor)Loren A. Honaas (Committee Member)Michael M. Neff (Committee Member)Zhiwu Zhang (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Program in Molecular Plant Sciences
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 258
- Identifiers
- 99901086433801842
- Language
- English
- Resource Type
- Dissertation