Líffræðifélag Íslands - biologia.is
Líffræðiráðstefnan 2017
Erindi/veggspjald / Talk/poster E30
Höfundar / Authors: Sigurður Trausti Karvelsson (1), Steinn Guðmundsson (1,2) & Óttar Rolfsson (1)
Starfsvettvangur / Affiliations: 1. Center for Systems Biology, University of Iceland, 2. School of Engineering and Natural Sciences, University of Iceland.
Kynnir / Presenter: Sigurður Trausti Karvelsson
Cancer is the second most common cause of death in the world. Metastasis is responsible for the mortality of cancer. For metastasis to take place, epithelial-mesenchymal transition (EMT) needs to take place first. EMT is a process where epithelial cells lose their adhesive phenotype and acquire a mesenchymal one.
Our group has been researching EMT in breast tissue. By using epithelial and mesenchymal cell lines, we employ a systems biology approach to build genome-scale metabolic models of those cell lines. The models are built with high-throughput data and the global human metabolic network RECON2 and they display how active different metabolic pathways are in each cell line. By comparing these computational models, we can observe EMT-related changes in metabolism.
This year, our group published results from this modeling based on RNA sequencing, metabolomics and microarray datasets and found that less usage of glycolysis and OXPHOS and increased usage of anaplerosis and fatty acid oxidation were associated with EMT. Recently, we acquired a proteomics dataset for our cell lines and wanted to repeat the previous study using that data.
In summary, we compared genome scale metabolic models for EMT in breast tissue constrained with microarray data and proteomics data. Although the datasets are from different sources, the metabolic phenotypes of the models are similar. However, the minor differences between the models are interesting and show that data type used for metabolic modeling could be important.