“Modeling of Plant Responses to the Environment Through Gene Regulatory Networks Inference”

Abstract

The huge data sets in biology, mainly due to the exponential development of high-throughput technologies and the growing computational power, represent a big challenge, usually not related with the acquisition of the data, but with the subsequent activities such as data processing, analysis, knowledge generation and getting insights for the research questions of interest. In such a sense, the approach of inferring gene regulatory networks (GRNs) has contributed importantly to understand functioning of living organisms. Because of the global population increment and climate change pose a challenge to worldwide crop production, there is a need to intensify agricultural production in a sustainable manner and to find solutions to combat abiotic stress, pathogens and pests. How plants respond to environmental changes, and how such knowledge could engender new technologies, for example, to increase crop yields, are issues that could be addressed using GRNs. Additionally, beneficial plant-microbe interactions represent a promising sustainable solution to improve agricultural production, therefore the study of such interactions becomes relevant. The research described in this thesis attempts to model plant responses to environmental changes, specifically salinity and pathogens, through GRNs inference. For the GRNs inference different evolutionary algorithms were utilized and the mathematical model used to represents the GRNs were threshold Boolean networks. The first chapter of this thesis addresses the theoretical framework, the study model and the objectives. The second chapter of this thesis described and characterized for the first time the mechanism used by the well-known beneficial bacterium Paraburkholderia 5 phytofirmans PsJN to protect Arabidopsis thaliana plants against a common pathogenic bacterium (Pseudomonas syringae DC3000). Results at the phenotypic, biochemical and transcriptional level were published and constitutes a contribution to the development and application of biopesticides based on beneficial bacteria. The third chapter further explores the regulatory mechanism of the defense response and induced systemic resistance (ISR), triggered by strain PsJN in Arabidopsis. To achieve this, a GRN underling ISR response was inferred using empirical time-series data of certain defense-related genes, differential evolution algorithm and threshold Boolean networks. The fourth chapter tackles the study of ISR response from a genome-wide point of view. A transcriptomic analysis was performed to understand global changes in gene expression of plants primed by strain PsJN and infected with P. syringae DC3000, in contrast with non-primed plants. The fifth and final chapter aimed at inferring a GRN involved in the underlying salt stress response in Arabidopsis plants using transcriptomic time-series data, genetic algorithms and threshold Boolean networks to better understand the regulatory process under saline growth conditions with the final goal of developing crops with enhanced tolerance to this important environmental threat