H Fouladiha1, S-A Marashi1, M A Shokrgozar2. 1. Department of Biotechnology, College of Science, University of Tehran, Tehran, 1417614411, Iran. 2. National Cell Bank of Iran, Pasteur Institute of Iran, Tehran, 1316943551, Iran.
Abstract
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.
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.
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