Public defence in Computer and Information Science, M.Sc Kari Nousiainen

Title of the doctoral thesis: Computational analysis and modeling of high-throughput data to understand T-helper cell differentiation

Opponent: Professor Tom Michoel, University of Bergen, Norway
Custos: Assistant Professor Harri Lähdesmäki, Aalto University School of Science, Department of Computer Science

The public defence will be organised on campus.

The thesis is publicly displayed 10 days before the defence in the publication archive Aaltodoc of Aalto University

Electronic thesis

Public defence announcement

T-helper cells steer the adaptive immunity. During the immune response, naïve T-helper cells, guided by cytokines in their environment, differentiate into sub-cell types, each with its own functions. In differentiation, intracellular systems change the epigenetics of the cell, as well as the expressions of genes and proteins, and therefore the functioning of the cell at the molecular level. Large-scale experimental observations of the molecules of the cells can be produced using high-throughput measurement techniques. The data produced with them, and the related methods of bioinformatics and computational biology have been widely applied. The objective of the dissertation research was: 1) To identify and characterize the expressions of genes and proteins of cells differentiating into the cell types iTreg and Th17, as well as changes in cell function in the early stages of differentiation using bioinformatics methods. 2) Develop a computational method and tool that identifies the enrichment of single-nucleotide polymorphisms, or SNPs, associated with the given traits in the given genome regions. 3) Develop computational methods for modeling the dynamic regulatory systems of the cell.

The dissertation profiled changes in gene and protein expressions in iTreg and Th17 cells in the early stages of differentiation. In addition, the binding of transcription factor STAT3 to the genome in cells that are in the process of differentiation into the Th17 cell type was investigated, as well as the enrichment of SNPs associated with different characteristics to the sites of STAT3 binding was studied. The analysis method was developed into an easy-to-use tool in the R programming language. The dissertation also developed a computational method for inferring a mechanistic ordinary differential equation model from time series data that describes the regulatory system of cell differentiation. The system in question may have time-dependent structural rewiring. Finally, the dissertation investigated the modelling of the interaction between enhancers and transcription factors using differential equations and investigated what kind of biological experiments modeling requires.

The results presented in the dissertation help to better understand the differentiation of T-helper cells. The developed methods and tools are freely available. They can facilitate work of bioinformaticians and researchers.

Contact details of the doctoral student: [email protected], 0503416991