Theses

Thesis by Maxime Multari (2024 - 2026). In this project, we propose to reconstructe a global network of plant molecular interactions, and developing multi-omics integration strategies coupled with network inference modeling.

Thesis by Chloé Weckel (PRC, 2024-2026). Following on from the IMAGO exploratory project funded by DIGIT-BIO, this thesis continues the interdisciplinary development of new formalisms to describe the spatio-temporal dynamics of cell signalling in the context of reproductive control.

Thesis by Alexandre Asset (BREED /MIA-PS, 2024 - 2026). Building on the work of EPINUM, this thesis proposes to investigate the most appropriate AI approaches that integrate epigenetic data into phenotype prediction models.

Thesis by François Victor (MIA Paris-Saclay, GQE Le Moulon, 2024-2026). The purpose of this work is to develop statistical computer learning methods to predict the performance of maize hybrids in different environments.

Thesis by Pamela Romero Jofre (2024-2026, PRC). This thesis continues the IMAGO exploratory project’s work on the functioning of hormonal receptor signalling pathways in mammals to improve understanding of reproduction. It seeks ways to facilitate the computational handling of variability in multiple inferred networks.

Thesis by Elise Jorge (GenPhyse, 2023-2025). This thesis, at the interface between statistics, molecular biology and functional genomics, seeks to develop a differential analysis method for Hi-C data to identify regions in the 3-D genome structure where modifications occur.

Thesis by Koloina Rabemanantsoa (Toxalim, 2024-2027). At the heart of the Hepat’O Twin project, this thesis sets out to develop an in silico model of the hepatic metabolic network in order to explore the metabolic effects of exposure to food contaminants under conditions of nutritional stress.

Thesis by Margot Danglot (PEGASE, 2024-2027). This thesis, part of the InSilcow project, will propose and test feeding and reproduction strategies adapted to the characteristics of dairy cows and available on-farm resources, to optimise their production and reproduction performance, health and welfare.

Thesis by Maud Hofmann (MICALIS, 2023-2026). This thesis seeks to develop experimental and theoretical methods to measure the cost of maintaining and executing synthetic genetic circuits inside cells. The results will provide a better understanding of ecosystem functions in microbial communities and division of labor in multicelllular organisms.

Thesis by Sihan Xie (GABI, 2023-2026). Deep learning (DL) methods are being increasingly used to build phenotype predictive models based on genotype data in the study of human diseases and production traits for genomic selection in domestic animals. These models require computer training with numerous data sets that are not always available. This thesis will address this limitation by proposing a novel method of simulating genotype data.