2 research outputs found
Modeling Functional Modules Using Statistical and Machine Learning Methods
Understanding the aspects of the cell functionality that account for disease or drug action
mechanisms is the main challenge for precision medicine. In spite of the increasing availability of
genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene
expression and the understanding of their contribution to the molecular mechanisms that ultimately
account for the phenotype studied. Over the last decade, different computational and mathematical
models have been proposed for pathway analysis. However, they are not taking into account the
dynamic mechanisms contained by pathways as represented in their layout and the interactions
between genes and proteins. In this thesis, I present two slightly different mathematical models to
integrate human transcriptomic data with prior knowledge of signalling and metabolic pathways to
estimate the Mechanistic Pathway Activities (MPAs). MPAs are continuous and individual level
values that can be used with machine learning and statistical methods to determine biomarkers for
the early diagnosis and subtype classification of the diseases, and also to suggest potential
therapeutic targets for individualized therapeutic interventions.
The overall objective is, developing new and advanced systems biology approaches to
propose functional hypotheses that help us to understand and interpret the complex mechanism of
the diseases. These mechanisms are crucial for robust personalized drug treatments and predict
clinical outcomes. First, I contributed to the development of a method which is designed to extract
elementary sub-pathways from a signalling pathway and to estimate their activity. Second, this
algorithm adapted to metabolic modules and it is implemented as a webtool. Third, the method
used to reveal a pan-cancer metabolic landscape. In this study, I analyzed the metabolic module
profile of 25 different cancer types and the method is also validated using different computational
and experimental approaches. Each method developed in this thesis was benchmarked against
the existing similar methods, evaluated for their sensitivity and specificity, experimentally validated
when it is possible and used to predict clinical outcomes of different cancer types. The research
described in this thesis and the results obtained were published in different systems biology and
cancer-related peer-reviewed journals and also in national newspapers