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. 2002 Aug;83(2):646–662. doi: 10.1016/S0006-3495(02)75198-9

Description and analysis of metabolic connectivity and dynamics in the human red blood cell.

Kenneth J Kauffman 1, John David Pajerowski 1, Neema Jamshidi 1, Bernhard O Palsson 1, Jeremy S Edwards 1
PMCID: PMC1302176  PMID: 12124254

Abstract

The human red blood cell (hRBC) metabolic network is relatively simple compared with other whole cell metabolic networks, yet too complicated to study without the aid of a computer model. Systems science techniques can be used to uncover the key dynamic features of hRBC metabolism. Herein, we have studied a full dynamic hRBC metabolic model and developed several approaches to identify metabolic pools of metabolites. In particular, we have used phase planes, temporal decomposition, and statistical analysis to show hRBC metabolism is characterized by the formation of pseudoequilibrium concentration states. Such equilibria identify metabolic "pools" or aggregates of concentration variables. We proceed to define physiologically meaningful pools, characterize them within the hRBC, and compare them with those derived from systems engineering techniques. In conclusion, systems science methods can decipher detailed information about individual enzymes and metabolites within metabolic networks and provide further understanding of complex biological networks.

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Selected References

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  1. Ataullakhanov F. I., Buravtsev V. N., Zhabotinskiĩ A. M., Norina S. B., Pichugin A. V. Vzaimodeĩstvie puti Embdena-Meiergofa i geksozomonofosfatnogo shunta v éritrotsitakh. Biokhimiia. 1981 Apr;46(4):723–731. [PubMed] [Google Scholar]
  2. Bailey J. E. Mathematical modeling and analysis in biochemical engineering: past accomplishments and future opportunities. Biotechnol Prog. 1998 Jan-Feb;14(1):8–20. doi: 10.1021/bp9701269. [DOI] [PubMed] [Google Scholar]
  3. Brumen M., Heinrich R. A metabolic osmotic model of human erythrocytes. Biosystems. 1984;17(2):155–169. doi: 10.1016/0303-2647(84)90006-6. [DOI] [PubMed] [Google Scholar]
  4. Edwards J. S., Palsson B. O. Multiple steady states in kinetic models of red cell metabolism. J Theor Biol. 2000 Nov 7;207(1):125–127. doi: 10.1006/jtbi.2000.2165. [DOI] [PubMed] [Google Scholar]
  5. Heinrich R., Rapoport S. M., Rapoport T. A. Metabolic regulation and mathematical models. Prog Biophys Mol Biol. 1977;32(1):1–82. [PubMed] [Google Scholar]
  6. Heinrich R., Rapoport T. A. Linear theory of enzymatic chains; its application for the analysis of the crossover theorem and of the glycolysis of human erythrocytes. Acta Biol Med Ger. 1973;31(4):479–494. [PubMed] [Google Scholar]
  7. Heinrich R., Rapoport T. A. Mathematical analysis of multienzyme systems. II. Steady state and transient control. Biosystems. 1975 Jul;7(1):130–136. doi: 10.1016/0303-2647(75)90050-7. [DOI] [PubMed] [Google Scholar]
  8. Holzhütter H. G., Jacobasch G., Bisdorff A. Mathematical modelling of metabolic pathways affected by an enzyme deficiency. A mathematical model of glycolysis in normal and pyruvate-kinase-deficient red blood cells. Eur J Biochem. 1985 May 15;149(1):101–111. doi: 10.1111/j.1432-1033.1985.tb08899.x. [DOI] [PubMed] [Google Scholar]
  9. Holzhütter H. G., Schuster R., Buckwitz D., Jacobasch G. Mathematical modelling of metabolic pathways affected by an enzyme deficiency. Biomed Biochim Acta. 1990;49(8-9):791–800. [PubMed] [Google Scholar]
  10. Jacobasch G., Holzhütter H. G., Gerth C. Mathematical model of pyruvate kinase of chicken erythrocytes. Biomed Biochim Acta. 1983;42(11-12):S289–S290. [PubMed] [Google Scholar]
  11. Jacobasch G., Holzhütter H., Bisdorf A. The energy metabolism of pyruvate kinase deficient red blood cells. Biomed Biochim Acta. 1983;42(11-12):S268–S272. [PubMed] [Google Scholar]
  12. Jacobasch G., Rapoport S. M. Hemolytic anemias due to erythrocyte enzyme deficiencies. Mol Aspects Med. 1996 Apr;17(2):143–170. doi: 10.1016/0098-2997(96)88345-2. [DOI] [PubMed] [Google Scholar]
  13. Jamshidi N., Edwards J. S., Fahland T., Church G. M., Palsson B. O. Dynamic simulation of the human red blood cell metabolic network. Bioinformatics. 2001 Mar;17(3):286–287. doi: 10.1093/bioinformatics/17.3.286. [DOI] [PubMed] [Google Scholar]
  14. Joshi A., Palsson B. O. Metabolic dynamics in the human red cell. Part I--A comprehensive kinetic model. J Theor Biol. 1989 Dec 19;141(4):515–528. doi: 10.1016/s0022-5193(89)80233-4. [DOI] [PubMed] [Google Scholar]
  15. Joshi A., Palsson B. O. Metabolic dynamics in the human red cell. Part II--Interactions with the environment. J Theor Biol. 1989 Dec 19;141(4):529–545. doi: 10.1016/s0022-5193(89)80234-6. [DOI] [PubMed] [Google Scholar]
  16. Joshi A., Palsson B. O. Metabolic dynamics in the human red cell. Part III--Metabolic reaction rates. J Theor Biol. 1990 Jan 9;142(1):41–68. doi: 10.1016/s0022-5193(05)80012-8. [DOI] [PubMed] [Google Scholar]
  17. Joshi A., Palsson B. O. Metabolic dynamics in the human red cell. Part IV--Data prediction and some model computations. J Theor Biol. 1990 Jan 9;142(1):69–85. doi: 10.1016/s0022-5193(05)80013-x. [DOI] [PubMed] [Google Scholar]
  18. Lee I. D., Palsson B. O. A comprehensive model of human erythrocyte metabolism: extensions to include pH effects. Biomed Biochim Acta. 1990;49(8-9):771–789. [PubMed] [Google Scholar]
  19. Lew V. L., Bookchin R. M. Volume, pH, and ion-content regulation in human red cells: analysis of transient behavior with an integrated model. J Membr Biol. 1986;92(1):57–74. doi: 10.1007/BF01869016. [DOI] [PubMed] [Google Scholar]
  20. McIntyre L. M., Thorburn D. R., Bubb W. A., Kuchel P. W. Comparison of computer simulations of the F-type and L-type non-oxidative hexose monophosphate shunts with 31P-NMR experimental data from human erythrocytes. Eur J Biochem. 1989 Mar 15;180(2):399–420. doi: 10.1111/j.1432-1033.1989.tb14662.x. [DOI] [PubMed] [Google Scholar]
  21. Mulquiney P. J., Kuchel P. W. Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: computer simulation and metabolic control analysis. Biochem J. 1999 Sep 15;342(Pt 3):597–604. [PMC free article] [PubMed] [Google Scholar]
  22. Palsson B. O., Joshi A., Ozturk S. S. Reducing complexity in metabolic networks: making metabolic meshes manageable. Fed Proc. 1987 Jun;46(8):2485–2489. [PubMed] [Google Scholar]
  23. Palsson B. O., Lightfoot E. N. Mathematical modelling of dynamics and control in metabolic networks. I. On Michaelis-Menten kinetics. J Theor Biol. 1984 Nov 21;111(2):273–302. doi: 10.1016/s0022-5193(84)80211-8. [DOI] [PubMed] [Google Scholar]
  24. Rae C., Berners-Price S. J., Bulliman B. T., Kuchel P. W. Kinetic analysis of the human erythrocyte glyoxalase system using 1H NMR and a computer model. Eur J Biochem. 1990 Oct 5;193(1):83–90. doi: 10.1111/j.1432-1033.1990.tb19307.x. [DOI] [PubMed] [Google Scholar]
  25. Rapoport S. M., Rapoport I., Schauer M., Heinrich R. The effect of pyruvate on glycolysis and the maintenance of adenine nucleotides in red cells. Acta Biol Med Ger. 1981;40(4-5):669–676. [PubMed] [Google Scholar]
  26. Rapoport T. A., Heinrich R. Mathematical analysis of multienzyme systems. I. Modelling of the glycolysis of human erythrocytes. Biosystems. 1975 Jul;7(1):120–129. doi: 10.1016/0303-2647(75)90049-0. [DOI] [PubMed] [Google Scholar]
  27. Rapoport T. A., Heinrich R., Rapoport S. M. The regulatory principles of glycolysis in erythrocytes in vivo and in vitro. A minimal comprehensive model describing steady states, quasi-steady states and time-dependent processes. Biochem J. 1976 Feb 15;154(2):449–469. doi: 10.1042/bj1540449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Schuster R., Holzhütter H. G., Jacobasch G. Interrelations between glycolysis and the hexose monophosphate shunt in erythrocytes as studied on the basis of a mathematical model. Biosystems. 1988;22(1):19–36. doi: 10.1016/0303-2647(88)90047-0. [DOI] [PubMed] [Google Scholar]
  29. Schuster R., Jacobasch G., Holzhütter H. G. Mathematical modelling of metabolic pathways affected by an enzyme deficiency. Energy and redox metabolism of glucose-6-phosphate-dehydrogenase-deficient erythrocytes. Eur J Biochem. 1989 Jul 1;182(3):605–612. doi: 10.1111/j.1432-1033.1989.tb14869.x. [DOI] [PubMed] [Google Scholar]
  30. Schuster R., Jacobasch G., Holzhütter H. Mathematical modelling of energy and redox metabolism of G6PD-deficient erythrocytes. Biomed Biochim Acta. 1990;49(2-3):S160–S165. [PubMed] [Google Scholar]
  31. Thorburn D. R., Kuchel P. W. Computer simulation of the metabolic consequences of the combined deficiency of 6-phosphogluconolactonase and glucose-6-phosphate dehydrogenase in human erythrocytes. J Lab Clin Med. 1987 Jul;110(1):70–74. [PubMed] [Google Scholar]
  32. Werner A., Heinrich R. A kinetic model for the interaction of energy metabolism and osmotic states of human erythrocytes. Analysis of the stationary "in vivo" state and of time dependent variations under blood preservation conditions. Biomed Biochim Acta. 1985;44(2):185–212. [PubMed] [Google Scholar]
  33. Yoshida T., Dembo M. A thermodynamic model of hemoglobin suitable for physiological applications. Am J Physiol. 1990 Mar;258(3 Pt 1):C563–C577. doi: 10.1152/ajpcell.1990.258.3.C563. [DOI] [PubMed] [Google Scholar]

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