Indirect role of microRNAs and transcription factors in the regulation of important cancer genes: A network biology approach.

Cancer is one of the leading causes of death worldwide. Although the mechanisms of gene regulation in cancer have been the subject of intense investigation during the last decades, the precise role of regulatory processes in cancer is largely unknown. More specifically, it is not completely understood how microRNAs and transcription factors regulate and influence the cancer-related processes. In the present study, using cancer-specific biological networks we examine the role of microRNAs and transcription factors (TFs) in regulation of important cancer genes. The importance measures which are used in this study consider both network structure information and biological data on miRNA- and TF-based gene regulation. By analyzing cancer-specific PPI, signaling and metabolic networks, it was shown that microRNAs and transcription factors tend to regulate those genes which are in the neighborhood of important components of cancer-specific PPI, signaling, and metabolic networks. The role of microRNAs was found to be particularly important, which confirms our previously-published results on the importance of microRNAs in detecting important network components. Moreover, we highlight that the miRNAs appear to apply their function via regulating the "neighbors" of important cancer genes, which implies their indirect role in cancer, and presumably, in fine-tuning the effect of other cancer-related genes.

Reconstruction and validation of a constraint-based metabolic network model for bone marrow-derived mesenchymal stem cells.

OBJECTIVES: Over recent years, constraint-based modelling of metabolic networks has become increasingly popular; the models are suitable for system-level modelling of cell physiology. The goal of the present work was to reconstruct a constraint-based metabolic network model of bone marrow-derived mesenchymal stem cells (BMMSCs). MATERIALS AND METHODS: To reconstruct a BMMSC-specific metabolic model, transcriptomic data of BMMSCs, and additionally, the human generic metabolic network model (Recon1) were used. Then, using the mCADRE algorithm, a draft metabolic network was reconstructed. Literature and proteomic data were subsequently used to refine and improve the draft. From this, iMSC1255 was derived to be the metabolic network model of BMMSCs. RESULTS: iMSC1255 has 1255 genes, 1850 metabolites and 2288 reactions. After including additional constraints based on previously reported experimental results, our model successfully predicted BMMSC growth rate and metabolic phenotypes. CONCLUSIONS: Here, iMSC1255 is introduced to be the metabolic network model of bone marrow-derived mesenchymal stem cells. Based on current knowledge, this is the first report on genome-scale reconstruction and validation of a stem cell metabolic network model.