|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
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
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
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