TY  - GEN
N2  - In this thesis, I study how biological prior knowledge and high throughput biological data can
be systematically integrated to yield mechanistic biological insights. I focused the scope of
my work mainly on signalling pathways and metabolism, especially how these two biological
functions interact and control each other. The overall goal of this work is to better
characterise the molecular driver of complex diseases and chronic health conditions such as
cancer, metabolic syndromes and fibrosis. Indeed, if we can better and more systematically
understand these conditions, we may be able to design better, more targeted treatments and
even prevent them more efficiently.
In the first chapter, I draw a state of the art of multi-omic data generation and how to analyze
them in mechanistic contexts. What we call omic data are datasets where the abundance of
hundred to thousand unique biological molecules are measured in parallel. Then, in the
second chapter, I present a collection of scientific studies where I could learn and apply the
principles detailed in the first chapter. In the third chapter, I present my attempt at developing
a way to systematically analyse and integrate multiple types of omic data together. The
resulting tool, named COSMOS, is presented in the context of a kidney cancer study using
multiple types of omic data generated from a cohort of patients. In the final chapter, I present
a tool called ocEAn, which aims at estimating metabolic enzyme activity changes from
metabolomic data.
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/31479/
A1  - Dugourd, Aurelien
ID  - heidok31479
Y1  - 2022///
TI  - Bridging the gap between signalling
and metabolism through functional and
mechanistic analysis of multi-omic data
CY  - Heidelberg
AV  - public
ER  -