Mathematical modelling of metabolism

Current Opinion in Plant Biology

Volumn 3, Issue 3, 2000, Pages 249-253

  Giersch, Christoph ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0034119624     PISSN: 13695266     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1369-5266(00)00072-8     Document Type: Review

References (51)

1. Bailey J.E. Mathematical modeling and analysis of biochemical engineering: past accomplishments and future opportunities. Biotechnol Prog. 14:1998;8-20. This is a vivid, personal review of mathematical modelling that addresses the question of why models are useful in the modelling of metabolism. A hierarchy of modelling approaches in metabolic studies is also presented and a pronounced gap between theory and practice is identified.
2. Nielsen J. Metabolic engineering: techniques for analysis of targets for genetic manipulations. Biotechnol Bioeng. 58:1998;125-132.
3. Gershenfeld N. The Nature of Mathematical Modeling. 1999;Cambridge University Press, Cambridge, UK.
4. Bernhardt K., Trissl H.W. Theories for kinetics and yields of fluorescence and photochemistry: how, if at all, can different models of antenna organization be distinguished experimentally? Biochim Biophys Acta. 1409:1999;125-142.
5. Stirbet A., Govindjee Strasser B.J., Strasser R.J. Chlorophyll a fluorescence induction in higher plants: modelling and numerical simulation. J Theor Biol. 193:1998;131-151.
6. Lazar D., Brokes M., Naus J., Dvorak L. Mathematical modelling of 3-(3′,4′-dichlorophenyl)-1,1-dimethylurea action in plant leaves. J Theor Biol. 191:1998;79-86.
7. 2 exchange model including gaseous phase and leaf mesophyll separated by irregular interface. J Theor Biol. 196:1999;115-128.
8. 2 diffusion for leaf photosynthesis. J Theor Biol. 188:1997;143-151.
9. 4 photosynthesis. Photosynth Res. 49:1996;195-208.
10. Cornish-Bowden A., Eisenthal R. Computer simulation as a tool for studying metabolism and drug design. Cornish-Bowden A., Cárdenas M.L. Technological and Medical Implications of Metabolic Control Analysis. 2000;165-172 Kluwer Academic Publishers, Dordrecht.
11. Pettersson G. Control properties of the Calvin photosynthesis cycle at physiological carbon dioxide concentrations. Biochim Biophys Acta. 1322:1997;173-182.
12. Pettersson G., Ryde-Pettersson U. A mathematical model of the Calvin photosynthesis cycle. Eur J Biochem. 175:1988;661-672.
13. 2 fixation as an example for general control mechanisms in metabolic cycles. BioSystems. 51:1999;79-93.
14. Poolman M.G., Fell D.A., Thomas S. Modelling photosynthesis and its control. J Exp Botany. 51:2000;319-328. This paper presents a fresh attempt to model the Calvin cycle and associated reactions by combining kinetic modelling with optimisation and metabolic control analysis. The combination of these methods make the modelling results more convincing by far than could have been the case for conventional kinetic modelling alone.
15. Pearcy R.W., Gross L.J., He D. An improved dynamic model of photosynthesis for estimation of carbon gain in sunfleck light regimes. Plant Cell Environ. 20:1997;411-424.
16. Kirschbaum M.U.F., Küppers M., Schneider H., Giersch C., Noe S. Modelling photosynthetic carbon gain in fluctuating light, with inclusion of stomatal conductance, Rubisco activation, induction of RuBP regeneration and pools of key photosynthetic intermediates. Planta. 204:1997;16-26.
17. Theobald U., Mailinger W., Baltes M., Rizzi M., Reuss M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnol Bioeng. 55:1997;305-316.
18. Rizzi M., Baltes M., Theobald U., Reuss M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnol Bioeng. 55:1997;592-608.
19. •].
20. •].
21. •]. These papers provide new insights into the regulation of 2,3-bisphosphoglycerate metabolism.
22. Shabala S., Delbourgo R., Newman I. Observations of bifurcations and chaos in plant physiological responses to light. Aust J Plant Physiol. 24:1997;91-96.
23. Blasius B., Neff R., Beck F., Lüttge U. Oscillatory model of crassulacean acid metabolism with a dynamic hysteresis switch. Proc R Soc Lond Biol Sci. 266:1999;93-101.
24. Heinrich R., Schuster S. The Regulation of Cellular Systems. 1996;Chapman & Hall, New York.
25. Fell D.A. Understanding the Control of Metabolism. 1997;Portland Press, London.
26. Cornish-Bowden A. Fundamentals of Enzyme Kinetics, revised edn. 1995;Portland Press, London.
27. Fell D.A. Metabolic control analysis - a survey of its theoretical and experimental development. Biochem J. 152:1992;313-330.
28. Cornish-Bowden A., Cárdenas M.L. Technological and Medical Implications of Metabolic Control Analysis. Proceedings of the NATO Advanced Research Workshop: Apr 10-16, 1999; Visegrád, Hungary. 2000;Kluwer Academic Publishers, Dordrecht.
29. Thomas S., Mooney P.J.F., Burell M.M., Fell D.A. Finite change analysis of glycolytic intermediates in tuber tissue of lines of transgenic potato (Solanum tuberosum) overexpressing phosphofructokinase. Biochem J. 322:1997;111-117.
30. Thomas S., Mooney P.J.F., Burell M.M., Fell D.A. Metabolic control analysis of glycolysis in tuber tissue of potato (Solanum tuberosum): explanation for the low control coefficient of phosphofructokinase over respiratory flux. Biochem J. 322:1997;119-127.
31. Harwood J.L., Ramli U.S., Page R.A., Ouant P.A. Modelling lipid metabolism in plants: a slippery problem? Biochem Soc Trans. 27:1999;285-289.
32. Hatzimanikatis V., Bailey J.E. Effects of spatiotemporal variations on metabolic control: approximate analysis using (log)linear kinetic models. Biotechnol Prog. 54:1997;91-104.
33. Savageau M.A. Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology. 1976;Addison-Wesley, Reading, Massachusetts.
34. Giersch C. Determining elasticities in situ. Cornish-Bowden A., Cárdenas M.L. Technological and Medical Implications of Metabolic Control Analysis. 2000;283-288 Kluwer Academic Publishers, Dordrecht.
35. Kell D.B., Mendes P. Snapshots of systems: metabolic control analysis and biotechnology in the post-genomic era. Cornish-Bowden A., Cárdenas M.L. Technological and Medical Implications of Metabolic Control Analysis. 2000;3-25 Kluwer Academic Publishers, Dordrecht.
36. Fell D.A., Thomas S. Control of flux: the requirement for multisite modulation. Biochem J. 311:1995;35-39.
37. Cornish-Bowden A., Hofmeyr J.H.S., Cárdenas M.L. Strategies for manipulating metabolic fluxes in biotechnology. Bioorg Chem. 23:1995;439-449.
38. Bonarius H.P.J., Schmid G., Tramper J. Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. Trends Biotech. 15:1997;308-314.
39. Schuster S., Hilgetag R. On elementary flux modes in biochemical reaction systems at steady state. J Biol Syst. 2:1994;165-182.
40. 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. 17:1999;53-60. This is a valuable review of the concept of elementary flux modes and its applications. The text gives several examples that illustrate the use and scope of this approach.
41. 13C-NMR, MS and metabolic flux balancing in biotechnological research. Quart Rev Biophys. 31:1998;41-106.
42. Wittmann C., Heinzle E. Mass spectrometry for metabolic flux analysis. Biotechnol Bioeng. 62:1999;739-750.
43. Klapa M.I., Park S.M., Sinskey A.J., Stephanopoulos G. Metabolite and isotopomer balancing in the analysis of metabolic cycles: I. Theory. Biotechnol Bioeng. 62:1999;375-391.
44. Park S.M., Klapa M.I., Sinskey A.J., Stephanopoulos G. Metabolite and isotopomer balancing in the analysis of metabolic cycles: II. Applications. Biotechnol Bioeng. 62:1999;392-401.
45. Wiechert W., Möllney M., Isermann N., Wurzel M., de Graaf A.A. Bidirectional reactions steps in metabolic networks. Part III. Explicit solution and analysis of isotopomer labeling systems. Biotechnol Bioeng. 66:1999;69-85. This paper takes a significant step towards identifying fluxes in pathways that have bidirectional reactions, essentially by defining new classes of isotopomers, the cumomers.
46. ••].
47. Mendes P., Kell D.B. Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics. 14:1998;869-883.
48. Sauro H.M. SCAMP: a general-purpose simulator and metabolic control analysis program. Comput Applic Biosci. 9:1993;441-450.
49. Goryanin I., Hodgman T.C., Selkov E. Mathematical simulation and analysis of cellular metabolism and regulation. Bioinformatics. 15:1999;749-758.
50. Ogata H., Goto S., Fujibuchi W., Kanehisa M. Computation with the KEGG pathway database. BioSystems. 47:1998;119-128. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database contains maps of metabolic pathways in addition to genomic sequence information. It uses various tools to compare pathways and predict functional enzyme sequences from genomic sequence data.
51. Varner J., Ramkrishna D. The nonlinear analysis of cybernetic models. Guidelines for model formulation. J Biotechnol. 71:1999;67-103.