Date of Award:

8-2021

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Plants, Soils, and Climate

Committee Chair(s)

Rakesh Kaundal

Committee

Rakesh Kaundal

Committee

Nicholas Flann

Committee

John G. Carman

Abstract

The Citrus fruit industry in the United States has been affected during the last two decades because of the outbreak of the citrus greening disease, also known as Huanglongbing (HLB). Many people and organizations are working in therapeutics to help mitigate the impact of this disease, unfortunately there is not a cure yet. There are many mechanisms that needs to be understood about this disease, especially at the molecular level. Not only HLB, but many other infectious diseases are controlled by the interaction of proteins from the host (Citrus in this case) and from the pathogen that causes the disease. When these interactions occur, the normal activity of the plant host is altered and then the plant start to develop more and more ill severe symptoms.

The purpose of this research was to implement diverse computational methods to predict those interactions accurately. We applied several methods successfully used in other infectious diseases, and developed novel techniques including the Artificial Intelligence models. After a comprehensive analysis of our results, we identified some candidate genes that we believe are key HLB players which could be genetically modified to enhance resistance in Citrus plants.

The models developed from this study were implemented as web-based tools for real time use by the biologists and other stakeholders. The knowledge gained from this study can further be used to understand plant-pathogen interactions in other agricultural systems of economic importance.

Available for download on Saturday, August 01, 2026

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