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Abstract

Understanding cell signaling is probably one of the biggest challenges in modern biology. Thanks to the advance in new technologies, like next generation sequencing and other high throughput techniques, commonly referred as omics technologies, researches can generate great amounts of comprehensive biological data. More recently, these technologies have advanced to the point where one can analyze genes or proteins in single cells, even with spatial resolution. The ability of these approaches to generate great amounts of data requires of complementary techniques to analyze it. Analyzing and contextualizing this amounts of data has provided great insight and development in our current understanding and treatment of many diseases. Current research on disease mechanisms focuses mainly on molecular processes in order to understand the underlying systems driving them. Many approaches have so far focused on intracellular signaling and it has not been until recent years that the role of cell-to-cell communication in health disorders has gained importance. With this goal in mind, I will be presenting approaches to model and analyze biological data accounting for intra- and intercellular communication. The models and analyses presented combine omics data with prior biological knowledge. For this, I rely and our in-house resource OmniPath to extract the relevant intra- and intercellular interactions.The analytical approaches presented in this thesis range from the more classical differential expression and gene set enrichment analysis to more advanced and recent machine learning methods. Thanks to the different strategies applied across different settings, I was able to extract relevant biological insights with applications in clinical and biological research. Despite the approaches presented in this thesis being mainly focused on cancer, these surely can be further extended and applicable in many other contexts.