, Volume 70, Issue 1, pp 331–338 | Cite as

Applications of a metabolic network model of mesenchymal stem cells for controlling cell proliferation and differentiation

  • Hamideh Fouladiha
  • Sayed-Amir Marashi
  • Mohammad Ali Shokrgozar
  • Mehdi Farokhi
  • Amir Atashi
Original Article


Mesenchymal stem cells (MSCs) can be isolated from several tissues of adults. In addition, MSCs have the potential of differentiation into several cell types. Therefore, MSCs are very useful in stem cell therapy and regenerative medicine. MSCs have also been used as gene or protein carriers. As a result, maintaining MSCs in a desirable metabolic state has been the subject of several studies. Here, we used a genome scale metabolic network model of bone marrow derived MSCs for exploring the metabolism of these cells. We analyzed metabolic fluxes of the model in order to find ways of increasing stem cell proliferation and differentiation. Consequently, the experimental results were in consistency with computational results. Therefore, analyzing metabolic models was proven to be a promising field in biomedical researches of stem cells.


Metabolic networks Mesenchymal stem cell (MSC) Proliferation Differentiation Modeling 



We would like to acknowledge the financial support of University of Tehran for this research under grant number 28791/1/2.

Supplementary material

10616_2017_148_MOESM1_ESM.docx (183 kb)
Supplementary material 1 (DOCX 183 kb)


  1. Altamirano C, Paredes C, Illanes A, Cairo J, Godia F (2004) Strategies for fed-batch cultivation of t-PA producing CHO cells: substitution of glucose and glutamine and rational design of culture medium. J Biotechnol 110:171–179CrossRefGoogle Scholar
  2. Antoniewicz MR (2015) Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biotechnol 42:317–325CrossRefGoogle Scholar
  3. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BØ, Herrgard MJ (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat Protoc 2:727–738CrossRefGoogle Scholar
  4. Burgard AP, Nikolaev EV, Schilling CH, Maranas CD (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14:301–312CrossRefGoogle Scholar
  5. Bürgermeister M, Birner-Grünberger R, Nebauer R, Daum G (2004) Contribution of different pathways to the supply of phosphatidylethanolamine and phosphatidylcholine to mitochondrial membranes of the yeast Saccharomyces cerevisiae. Biochim Biophys Acta 1686:161–168CrossRefGoogle Scholar
  6. Castro PM, Hayter PM, Ison AP, Bull AT (1992) Application of a statistical design to the optimization of culture medium for recombinant interferon-gamma production by Chinese hamster ovary cells. Appl Microbiol Biotechnol 38:84–90CrossRefGoogle Scholar
  7. Chang RL, Xie L, Xie L, Bourne PE, Palsson BØ (2010) Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol 6:e1000938CrossRefGoogle Scholar
  8. Fell DA, Small JR (1986) Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J 238:781–786CrossRefGoogle Scholar
  9. Fouladiha H, Marashi S-A (2017) Biomedical applications of cell-and tissue-specific metabolic network models. J Biomed Inform 68:35–49CrossRefGoogle Scholar
  10. Fouladiha H, Marashi SA, Shokrgozar MA (2015) Reconstruction and validation of a constraint-based metabolic network model for bone marrow-derived mesenchymal stem cells. Cell Prolif 48:475–485CrossRefGoogle Scholar
  11. Goldbeter A, Lefever R (1972) Dissipative structures for an allosteric model. Application to glycolytic oscillations. Biophys J 12:1302–1315CrossRefGoogle Scholar
  12. Grayson WL, Zhao F, Izadpanah R, Bunnell B, Ma T (2006) Effects of hypoxia on human mesenchymal stem cell expansion and plasticity in 3D constructs. J Cell Physiol 207:331–339CrossRefGoogle Scholar
  13. Gutierrez JM, Lewis NE (2015) Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling. Biotechnol J 10:939–949CrossRefGoogle Scholar
  14. Hadi M, Marashi SA (2014) Reconstruction of a generic metabolic network model of cancer cells. Mol BioSyst 10:3014–3021CrossRefGoogle Scholar
  15. Higgins J (1964) A chemical mechanism for oscillation of glycolytic intermediates in yeast cells. Proc Natl Acad Sci USA 51:989–994CrossRefGoogle Scholar
  16. Jerby L, Ruppin E (2012) Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 18:5572–5584CrossRefGoogle Scholar
  17. Karr JR et al (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401CrossRefGoogle Scholar
  18. King ZA, Lloyd CJ, Feist AM, Palsson BO (2015) Next-generation genome-scale models for metabolic engineering. Curr Opin Biotechnol 35:23–29CrossRefGoogle Scholar
  19. Nishijima M, Kuge O, Akamatsu Y (1986) Phosphatidylserine biosynthesis in cultured Chinese hamster ovary cells. I. Inhibition of de novo phosphatidylserine biosynthesis by exogenous phosphatidylserine and its efficient incorporation. J Biol Chem 261:5784–5789Google Scholar
  20. O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities. Cell 161:971–987CrossRefGoogle Scholar
  21. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248CrossRefGoogle Scholar
  22. Pattappa G, Heywood HK, de Bruijn JD, Lee DA (2011) The metabolism of human mesenchymal stem cells during proliferation and differentiation. J Cell Physiol 226:2562–2570CrossRefGoogle Scholar
  23. Pattappa G, Thorpe SD, Jegard NC, Heywood HK, de Bruijn JD, Lee DA (2013) Continuous and uninterrupted oxygen tension influences the colony formation and oxidative metabolism of human mesenchymal stem cells. Tissue Eng Part C 19:68–79CrossRefGoogle Scholar
  24. Poolman MG, Venkatesh KV, Pidcock MK, Fell DA (2004) A method for the determination of flux in elementary modes, and its application to Lactobacillus rhamnosus. Biotechnol Bioeng 88:601–612CrossRefGoogle Scholar
  25. Read EK, Bradley SA, Smitka TA, Agarabi CD, Lute SC, Brorson KA (2013) Fermentanomics informed amino acid supplementation of an antibody producing mammalian cell culture. Biotechnol Prog 29:745–753CrossRefGoogle Scholar
  26. Sá JV, Kleiderman S, Brito C, Sonnewald U, Leist M, Teixeira AP, Alves PM (2017) Quantification of metabolic rearrangements during neural stem cells differentiation into astrocytes by metabolic flux analysis. Neurochem Res 42:244–253CrossRefGoogle Scholar
  27. Saha R, Chowdhury A, Maranas CD (2014) Recent advances in the reconstruction of metabolic models and integration of omics data. Curr Opin Biotechnol 29:39–45CrossRefGoogle Scholar
  28. Sart S, Agathos SN, Li Y (2014) Process engineering of stem cell metabolism for large scale expansion and differentiation in bioreactors. Biochem Eng J 84:74–82CrossRefGoogle Scholar
  29. Shields DJ, Lehner R, Agellon LB, Vance DE (2003) Membrane topography of human phosphatidylethanolamine N-methyltransferase. J Biol Chem 278:2956–2962CrossRefGoogle Scholar
  30. Shlomi T, Cabili MN, Ruppin E (2009) Predicting metabolic biomarkers of human inborn errors of metabolism. Mol Syst Biol 5:263CrossRefGoogle Scholar
  31. Simeonidis E, Price ND (2015) Genome-scale modeling for metabolic engineering. J Ind Microbiol Biotechnol 42:327–338CrossRefGoogle Scholar
  32. Thiele I, Price ND, Vo TD, Palsson BØ (2005) Candidate metabolic network states in human mitochondria: impact of diabetes, ischemia, and diet. J Biol Chem 280:11683–11695CrossRefGoogle Scholar
  33. Varma A, Palsson BØ (1993) Metabolic capabilities of Escherichia coli II. Optimal growth patterns. J Theor Biol 165:503–522CrossRefGoogle Scholar
  34. Vozza A, Parisi G, De Leonardis F, Lasorsa FM, Castegna A, Amorese D, Marmo R, Calcagnile VM, Palmieri L, Ricquier D, Paradies E, Scarcia P, Palmieri F, Bouillaud F, Fiermonte G (2014) UCP2 transports C4 metabolites out of mitochondria, regulating glucose and glutamine oxidation. Proc Natl Acad Sci USA 111:960–965CrossRefGoogle Scholar
  35. Wanet A, Arnould T, Najimi M, Renard P (2015) Connecting mitochondria, metabolism, and stem cell fate. Stem Cells Dev 24:1957–1971CrossRefGoogle Scholar
  36. Wiback SJ, Palsson BØ (2002) Extreme pathway analysis of human red blood cell metabolism. Biophys J 83:808–818CrossRefGoogle Scholar
  37. Yang H, Roth CM, Ierapetritou MG (2009) A rational design approach for amino acid supplementation in hepatocyte culture. Biotechnol Bioeng 103:1176–1191CrossRefGoogle Scholar
  38. Yazdani SO, Hafizi M, Zali AR, Atashi A, Ashrafi F, Seddighi AS, Soleimani M (2013) Safety and possible outcome assessment of autologous Schwann cell and bone marrow mesenchymal stromal cell co-transplantation for treatment of patients with chronic spinal cord injury. Cytotherapy 15:782–791CrossRefGoogle Scholar
  39. Yizhak K, Gabay O, Cohen H, Ruppin E (2013) Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nat Commun 4:2632CrossRefGoogle Scholar
  40. Zhao F, Pathi P, Grayson W, Xing Q, Locke BR, Ma T (2005) Effects of oxygen transport on 3-D human mesenchymal stem cell metabolic activity in perfusion and static cultures: experiments and mathematical model. Biotechnol Prog 21:1269–1280CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Hamideh Fouladiha
    • 1
  • Sayed-Amir Marashi
    • 1
  • Mohammad Ali Shokrgozar
    • 2
  • Mehdi Farokhi
    • 2
  • Amir Atashi
    • 3
  1. 1.Department of Biotechnology, College of ScienceUniversity of TehranTehranIran
  2. 2.National Cell Bank of IranPasteur Institute of IranTehranIran
  3. 3.Stem Cell and Tissue Engineering Research CenterShahroud University of Medical SciencesShahroudIran

Personalised recommendations