The advent of new single-cell sequencing technologies now allows for a detailed and in-depth analysis of the gene regulatory mechanisms involved in human embryonic development. This advancement opens new perspectives in the complex and limited study of embryonic development. This thesis focuses on the computational modeling of development to better understand the gene regulatory mechanisms involved. To this end, we present SCIBORG, a method that infers Boolean networks modeling the stages of development of interest. By combining gene interactions from prior knowledge with observed gene expression in embryonic cells, Boolean models are learned, indicating the mechanisms involved in the studied developmental stages. We applied our method to two developmental stages of the trophectoderm and discovered distinct regulatory mechanisms in our models. Overall, this thesis proposes a method for modeling the regulatory mechanisms involved in a cell differentiation process, providing a valuable tool for studying human embryonic development.

Implementation of a PCR-free protocol to prepare DNAs for Illumina sequencing.
Development of SSV-Coverage, a program in Python 3 allowing the creation of a coverage graph.