Dynamic simulation of an in vitro multi-enzyme system

Ishii, Nobuyoshi; Suga, Yoshihiro; Hagiya, Akiko; Watanabe, Hisami; Mori, Hirotada; Yoshino, Masataka; Tomita, Masaru

doi:10.1016/j.febslet.2006.12.049

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Volume 581, Issue 3 , Pages 413-420, 6 February 2007

Dynamic simulation of an in vitro multi-enzyme system

Edited by Robert B. Russell

Nobuyoshi Ishii
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-8520, Japan
Yoshihiro Suga
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
Akiko Hagiya
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
Hisami Watanabe
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
Hirotada Mori
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
- Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
Masataka Yoshino
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
- Department of Biochemistry, Aichi Medical University School of Medicine, Nagakute 480-1195, Japan
Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-8520, Japan
- Corresponding author. Fax: +81 235 29 0809.

Received 7 August 2006; received in revised form 23 November 2006; accepted 25 December 2006. published online 12 January 2007.

Abstract

Parameters often are tuned with metabolite concentration time series data to build a dynamic model of metabolism. However, such tuning may reduce the extrapolation ability (generalization capability) of the model. In this study, we determined detailed kinetic parameters of three purified Escherichia coli glycolytic enzymes using the initial velocity method for individual enzymes; i.e., the parameters were determined independently from metabolite concentration time series data. The metabolite concentration time series calculated by the model using the parameters matched the experimental data obtained in an actual multi-enzyme system consisting of the three purified E. coli glycolytic enzymes. Thus, the results indicate that kinetic parameters can be determined without using an undesirable tuning process.

Keywords: Dynamic simulation, Mathematical model, Enzyme kinetics, Metabolism, Glycolysis

Abbreviations: ALD, aldolase (EC 4.1.2.13), DTT, dithiothreitol, F6P, fructose-6-phosphate, FDP, fructose-1,6-diphosphate, G3PDH, glycerol-3-phosphate dehydrogenase (EC 1.1.1.8), G6P, glucose-6-phosphate, G6PDH, glucose-6-phosphate dehydrogenase (EC 1.1.1.49), Glk, glucokinase (EC 2.7.1.2), IPTG, isopropyl-β-d-thiogalactopyranoside, MOPS, 3-morpholinopropanesulfonic acid, Pfk, phosphofructokinase (EC 2.7.1.11), Pgi, phosphoglucoisomerase (EC 5.3.1.9), TIM, triose phosphate isomerase (EC 5.3.1.1)