Lääkeainevaikutusten arviointi geenisäätelyverkoissa: Boolean mallinnus

Gene regulatory networks (GRNs) control the amount and the temporal patterns of gene products, both of which are crucial for the correct functioning of the living cells of an organism. In many diseases, such as cancer, biological processes controlled by GRNs are perturbed. Understanding the functioning of GRNs may lead to a better understanding of the mechanisms behind disease and ultimately to the identification of putative drug targets. The amount of information on the components of the GRNs and the interactions between them is increasing rapidly. Many modelling approaches have been applied to simulate the behaviour of GRNs. Boolean networks give qualitative predictions of the dynamic behaviour of the GRNs. They are applicable especially for large GRNs where all the mechanistic details of different reactions are not known. In this thesis, an analysis framework to predict the effects of drugs in the context of GRNs was developed. A network consisting of genes, drugs and biological processes was constructed based on knowledge in biological databases. The behaviour of the network was simulated with Boolean networks. To predict the effect of perturbing the network with a drug, an activation score was developed to estimate the activity of different components of the network before and after the perturbation. The method was applied to triple-negative breast cancer data to search for putative drug targets