Theses and Dissertations

ORCID

https://orcid.org/0000-0002-3772-6185

Issuing Body

Mississippi State University

Advisor

Popescu, George V.

Committee Member

Popescu, Sorina C.

Committee Member

Voit, Eberhard O.

Committee Member

Bohach, Gregory

Committee Member

Sepehrifar, Mohammad

Date of Degree

12-9-2022

Document Type

Dissertation - Open Access

Major

Biochemistry

Degree Name

Doctor of Philosophy (Ph.D)

College

College of Agriculture and Life Sciences

Department

Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology

Abstract

The work here presents two main parts. In the first part, chapters 1 – 3 focus on dynamical systems modeling in plant immunity, whereas chapters 4 – 6 describe contributions to computational modeling and analysis of proteomics and genomics data. Chapter 1 investigates dynamical and biochemical patterns of reversibly oxidized cysteines (RevOxCys) during effector-triggered immunity (ETI) in Arabidopsis, examines the regulatory patterns associated with Arabidopsis thimet oligopeptidase 1 and 2’s (TOP1 and TOP2), roles in the RevOxCys events during ETI, and analyzes the redox phenotype of the top1top2 mutant. The second chapter investigates the peptidome dynamics during ETI in wild-type (WT) and top1top2 mutant, introduces a novel method to learn the cleavage motif for TOPs and predicts and validates bioactive peptides association with TOPs activity. The third chapter examines gene expression dynamics during Systemic Acquired Resistance (SAR). Time-series clustering identifies unique oscillatory patterns in gene transcription associated with the early onset of SAR. It then describes a mathematical model using ordinary differential equations to represent WT's transcriptional dynamics. The second part of this dissertation explores imputation and statistical modeling for proteomics data analysis and proposes a network inference methodology for polymorphic cysteines. The fourth chapter analyzes the performance of linear models (limma) and the effect of imputation in proteomics data. It shows the advantage of data imputation over filtering and the benefit of using limma over t-test for the statistical decision of differences in means between conditions for different peptides, PTMs, etc. The fifth chapter proposes a statistical model for proteomics data analysis using mean-variance (M-V) trend modeling. It describes a gamma regression to model the dependency of the variance on the mean of observations. Finally, a Bayesian decision model is proposed; the model shows an improvement over existing methods in statistical decision performance. The sixth and final chapter describes a network inference procedure that identifies genetic dependencies between polymorphic cysteines. It models the interactions between cysteines (nodes) as signed edges for positive or inhibitory relations. It utilizes local network structures for inferences about the relationship between the cysteines. The algorithm exhibits stability and efficiency, converging rapidly to inferred solutions.

Sponsorship

National Science Foundation

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