Abstract
The development of high-throughput technologies and the resulting large-scale data sets have necessitated a systems approach to the analysis of metabolic networks. One way to approach the issue of complex metabolic function is through the calculation and interpretation of extreme pathways. Extreme pathways are a mathematically defined set of generating vectors that describe the conical steady-state solution space for flux distributions through an entire metabolic network. Herein, the extreme pathways of the well-characterized human red blood cell metabolic network were calculated and interpreted in a biochemical and physiological context. These extreme pathways were divided into groups based on such criteria as their cofactor and by-product production, and carbon inputs including those that 1) convert glucose to pyruvate; 2) interchange pyruvate and lactate; 3) produce 2,3-diphosphoglycerate that binds to hemoglobin; 4) convert inosine to pyruvate; 5) induce a change in the total adenosine pool; and 6) dissipate ATP. Additionally, results from a full kinetic model of red blood cell metabolism were predicted based solely on an interpretation of the extreme pathway structure. The extreme pathways for the red blood cell thus give a concise representation of red blood cell metabolism and a way to interpret its metabolic physiology.
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- Covert M. W., Schilling C. H., Famili I., Edwards J. S., Goryanin I. I., Selkov E., Palsson B. O. Metabolic modeling of microbial strains in silico. Trends Biochem Sci. 2001 Mar;26(3):179–186. doi: 10.1016/s0968-0004(00)01754-0. [DOI] [PubMed] [Google Scholar]
- Covert M. W., Schilling C. H., Palsson B. Regulation of gene expression in flux balance models of metabolism. J Theor Biol. 2001 Nov 7;213(1):73–88. doi: 10.1006/jtbi.2001.2405. [DOI] [PubMed] [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- Marcotte E. M. The path not taken. Nat Biotechnol. 2001 Jul;19(7):626–627. doi: 10.1038/90222. [DOI] [PubMed] [Google Scholar]
- 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]
- Ouzounis C. A., Karp P. D. Global properties of the metabolic map of Escherichia coli. Genome Res. 2000 Apr;10(4):568–576. doi: 10.1101/gr.10.4.568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Palsson B. The challenges of in silico biology. Nat Biotechnol. 2000 Nov;18(11):1147–1150. doi: 10.1038/81125. [DOI] [PubMed] [Google Scholar]
- Papin Jason A., Price Nathan D., Edwards Jeremy S., Palsson B Bernhard Ø. The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy. J Theor Biol. 2002 Mar 7;215(1):67–82. doi: 10.1006/jtbi.2001.2499. [DOI] [PubMed] [Google Scholar]
- Schilling C. H., Letscher D., Palsson B. O. Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol. 2000 Apr 7;203(3):229–248. doi: 10.1006/jtbi.2000.1073. [DOI] [PubMed] [Google Scholar]
- Schilling C. H., Palsson B. O. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J Theor Biol. 2000 Apr 7;203(3):249–283. doi: 10.1006/jtbi.2000.1088. [DOI] [PubMed] [Google Scholar]
- Schilling C. H., Palsson B. O. The underlying pathway structure of biochemical reaction networks. Proc Natl Acad Sci U S A. 1998 Apr 14;95(8):4193–4198. doi: 10.1073/pnas.95.8.4193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schilling C. H., Schuster S., Palsson B. O., Heinrich R. Metabolic pathway analysis: basic concepts and scientific applications in the post-genomic era. Biotechnol Prog. 1999 May-Jun;15(3):296–303. doi: 10.1021/bp990048k. [DOI] [PubMed] [Google Scholar]
- Schuster S., Dandekar T., Fell D. A. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 1999 Feb;17(2):53–60. doi: 10.1016/s0167-7799(98)01290-6. [DOI] [PubMed] [Google Scholar]
- Schuster S., Fell D. A., Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol. 2000 Mar;18(3):326–332. doi: 10.1038/73786. [DOI] [PubMed] [Google Scholar]
- 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]