Given the ever increasing cost of designing de novo molecules to target causes of diseases, and the huge amount of currently available biological data, the development of systematic explorative pipelines for drug development has become of paramount importance. In my thesis, I focused on drug repurposing, which is a paradigm that aims at identifying new therapeutic indications for known chemical compounds. Due to the already large collection of transcriptomic data -that is, related to protein production through the transcription of gene DNA sequences- which is publicly available, I investigated how to process in a transparent and controllable way this information about gene activity to screen molecules. The current state of research in drug development indicates that such generic approaches might considerably fasten the discovery of promising therapies, especially for neglected or rare diseases research. First, noting that transcriptomic measurements are the product of a complex dynamical system of co- and inter-gene activity regulations, I worked on integrating in an automated fashion diverse types of biological information in order to build a model of these regulations. That is where gene regulatory networks, and more specifically, Boolean networks, intervene. Such models are useful for both explaining observed transcription levels, and for predicting the result of gene activity perturbations through molecules. Second, these models allow online in silico drug testing. While using the predictive features of Boolean networks can be costly, the core assumption of this thesis is that, combining them with sequential learning algorithms, such as multi-armed bandits, might mitigate that effect, and help control the error rate in recommended therapeutic candidates. This is the drug testing procedure suggested throughout my PhD. The question of the proper integration of known side information about the chemical compounds into multi-armed bandits is crucial, and has also been investigated further. Finally, I applied part of my work to ranking different treatment protocols for neurorepair in the case of encephalopathy in premature infants. On the theoretical side, I also contributed to the design of an algorithm which is able to extend the drug testing procedure in a distributed way, for instance across several tested populations, disease models, or research teams.
DELAHAYE-DURIEZ Andrée - Directeur de Thèse - Université Paris Cité REMY Elisabeth - Rapporteur - Institut de Mathématiques de Marseille HONDA Junya Rapporteur - Kyoto University VILLOUTREIX Bruno - Examinateur - Université Paris CIté AZENCOTT Chloé-Agathe - Examinatrice - Mines Paris Tech THIEFFRY Denis - Examinateur - Institut de Biologie ENS de Paris KOOLEN Wouter Examinateur - Centrum Wiskunde & Informatica KAUFMANN Emilie - Membre invitée - INRIA Lille
Thesis of the team SCOOL defended on 09/09/2022
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