Özet:
Biological systems which can be represented as networks and graphs are highly dynamic and responsive to environmental and genetic perturbations in a time dependent manner. These networks are hierarchically organized and consist of tightly clustered groups of proteins that work together as part of a biological process or a complex to achieve a specific function in a cell. With the emergence of high-troughput dynamic datasets, dynamic data analysis became a challenge in systems biology with the other challenges such as representation of biological systems as networks and elucidation of graph properties of these networks biologically and integration of multi –omics datasets in order to extract biologically meaningful results. The aim of this thesis is to develop a novel metric of centrality to identify biologically important nodes and to develop novel approaches to investigate dynamic datasets. In the first part, a novel global metric of centrality, weighted sum of loads eigenvector centrality (WSL-EC), counting all eigenvectors was proposed to identify essential and biologically central nodes. WSL-EC was found to outperform in capturing biologically central nodes, such as pathogen-interacting, HIV-1, cancer, ageing, and disease-related genes and genes involved in immune system process and related to autoimmune diseases in the human interactome compared with other metrics of centrality. In the second part dynamic transcriptional response of S. cerevisiae cells to doxorubicin, which is used as chemotherapeutic reagent in the treatment of different types of cancer, was monitored by quantification of RNA transcripts in cells which were grown in a chemostat fermenter, through microarray technology. Resulting dynamic transcriptome data were investigated by using different approaches and integrating interactome and regulome. The clustering and analysis of the transcriptomic response of S. cerevisiae cells to doxorubicin indicated that the genes involved in DNA replication, mismatched repair, cell cycle and base excision repair pathways were affected and several transcriptional factors were identified. In the third part the data collected from literature related to the transcriptional response of yeast cells to DNA damage was similarly investigated and compared with the response to doxorubicin.
