Literature DB >> 15291812

A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics.

Liang Wu1, Weiming Wang, Wouter A van Winden, Walter M van Gulik, Joseph J Heijnen.   

Abstract

The control properties of biochemical pathways can be described by control coefficients and elasticities, as defined in the framework of metabolic control analysis. The determination of these parameters using the traditional metabolic control analysis relationships is, however, limited by experimental difficulties (e.g. realizing and measuring small changes in biological systems) and lack of appropriate mathematical procedures (e.g. when the more practical large changes are made). In this paper, the recently developed lin-log approach is proposed to avoid the above-mentioned problems and is applied to estimate control parameters from measurements obtained in steady state experiments. The lin-log approach employs approximative linear-logarithmic kinetics parameterized by elasticities and provides analytical solutions for fluxes and metabolite concentrations when large changes are made. Published flux and metabolite concentration data are used, obtained from a reconstructed section of glycolysis converting 3-phosphoglycerate to pyruvate [Giersch, C. (1995) Eur. J. Biochem. 227, 194-201]. With the lin-log approach, all data from different experiments can be combined to give realistic elasticity and flux control coefficient estimates by linear regression. Despite the large changes, a good agreement of fluxes and metabolite concentrations is obtained between the measured and calculated values according to the lin-log model. Furthermore, it is shown that the lin-log approach allows a rigorous statistical evaluation to identify the optimal reference state and the optimal model structure assumption. In conclusion, the lin-log approach addresses practical problems encountered in the traditional metabolic control analysis-based methods by introducing suitable nonlinear kinetics, thus providing a novel framework with improved procedures for the estimation of elasticities and control parameters from large perturbation experiments.

Entities:  

Mesh:

Year:  2004        PMID: 15291812     DOI: 10.1111/j.0014-2956.2004.04269.x

Source DB:  PubMed          Journal:  Eur J Biochem        ISSN: 0014-2956


  16 in total

1.  Systems biology towards life in silico: mathematics of the control of living cells.

Authors:  Hans V Westerhoff; Alexey Kolodkin; Riaan Conradie; Stephen J Wilkinson; Frank J Bruggeman; Klaas Krab; Jan H van Schuppen; Hanna Hardin; Barbara M Bakker; Martijn J Moné; Katja N Rybakova; Marco Eijken; Hans J P van Leeuwen; Jacky L Snoep
Journal:  J Math Biol       Date:  2008-02-16       Impact factor: 2.259

2.  The evolution of control and distribution of adaptive mutations in a metabolic pathway.

Authors:  Kevin M Wright; Mark D Rausher
Journal:  Genetics       Date:  2009-12-04       Impact factor: 4.562

Review 3.  Metabolic kinetic modeling provides insight into complex biological questions, but hurdles remain.

Authors:  Jonathan Strutz; Jacob Martin; Jennifer Greene; Linda Broadbelt; Keith Tyo
Journal:  Curr Opin Biotechnol       Date:  2019-03-07       Impact factor: 9.740

4.  Analysis of operating principles with S-system models.

Authors:  Yun Lee; Po-Wei Chen; Eberhard O Voit
Journal:  Math Biosci       Date:  2011-03-04       Impact factor: 2.144

Review 5.  The best models of metabolism.

Authors:  Eberhard O Voit
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2017-05-19

6.  Dominant-negative modification reveals the regulatory function of the multimeric cysteine synthase protein complex in transgenic tobacco.

Authors:  Markus Wirtz; Rüdiger Hell
Journal:  Plant Cell       Date:  2007-02-09       Impact factor: 11.277

7.  An enzyme-centric approach for modelling non-linear biological complexity.

Authors:  Chin-Rang Yang
Journal:  BMC Syst Biol       Date:  2008-08-01

8.  Hybrid dynamic/static method for large-scale simulation of metabolism.

Authors:  Katsuyuki Yugi; Yoichi Nakayama; Ayako Kinoshita; Masaru Tomita
Journal:  Theor Biol Med Model       Date:  2005-10-04       Impact factor: 2.432

9.  A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics.

Authors:  I Emrah Nikerel; Wouter A van Winden; Walter M van Gulik; Joseph J Heijnen
Journal:  BMC Bioinformatics       Date:  2006-12-21       Impact factor: 3.169

10.  Estimating parameters for generalized mass action models with connectivity information.

Authors:  Chih-Lung Ko; Eberhard O Voit; Feng-Sheng Wang
Journal:  BMC Bioinformatics       Date:  2009-05-11       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.