Reconstruction of Boolean gene regulatory network model based on pseudotime analysis of single-cell RNA sequencing data

Abstract

Over the past decade, data-driven reconstruction of gene regulatory network has been an important subject of study as a clue to the systems level understanding of the biological phenomena, but there was limitation on the amount of data available. However, developments in single cell technology and pseudotime analysis allowed single cell data regarded as time-series data. Especially, algorithm to infer the network with dynamics are being developed, beyond the reconstruction of network topology alone. In this thesis, a Boolean network reconstruction method based on pseudotime analysis of high-dimension single-cell RNA-seq data is proposed, and applied to the single-cell data of early colon cancer progression data. The reconstructed network is then analyzed if it can provide a systems level understanding of the tumorigenesis.