Parameter Estimation in Kinetic Models for Large Scale Metabolic Networks with Advanced Mathematical Programming Techniques

JIMENA DI MAGGIO; JUAN C. DIAZ RICCI; MARIA SOLEDAD DIAZ

COMPUTER AIDED CHEMICAL ENGINEERING

ELSEVIER

Lugar: Amsterdam; Año: 2010 vol. 28 p. 355 - 360

1570-7946

In this work, we formulate a parameter estimation problem for a large-scale dynamic metabolic network. The DAE system represents the dynamic model for the Embden- Meyerhof-Parnas pathway, the phosphotransferase system and the pentose-phosphate pathway of Escherichia coli K-12 W3110 (Chassagnole et al., 2002), with modifications on several enzyme kinetics and the addition of fermentation reactions. Model parameters have been estimated based on recently published experimental data for this strain. Most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network (Di Maggio et al., 2009a,b). Eleven kinetic parameters, including maximum reaction rates, inhibition and halfsaturation constants, have been estimated with good agreement with available experimental data. modifications on several enzyme kinetics and the addition of fermentation reactions. Model parameters have been estimated based on recently published experimental data for this strain. Most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network (Di Maggio et al., 2009a,b). Eleven kinetic parameters, including maximum reaction rates, inhibition and halfsaturation constants, have been estimated with good agreement with available experimental data. modifications on several enzyme kinetics and the addition of fermentation reactions. Model parameters have been estimated based on recently published experimental data for this strain. Most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network (Di Maggio et al., 2009a,b). Eleven kinetic parameters, including maximum reaction rates, inhibition and halfsaturation constants, have been estimated with good agreement with available experimental data. modifications on several enzyme kinetics and the addition of fermentation reactions. Model parameters have been estimated based on recently published experimental data for this strain. Most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network (Di Maggio et al., 2009a,b). Eleven kinetic parameters, including maximum reaction rates, inhibition and halfsaturation constants, have been estimated with good agreement with available experimental data. Escherichia coli K-12 W3110 (Chassagnole et al., 2002), with modifications on several enzyme kinetics and the addition of fermentation reactions. Model parameters have been estimated based on recently published experimental data for this strain. Most sensitive parameters have been ranked by performing global sensitivity analysis on the dynamic metabolic network (Di Maggio et al., 2009a,b). Eleven kinetic parameters, including maximum reaction rates, inhibition and halfsaturation constants, have been estimated with good agreement with available experimental data.