Literature DB >> 11792451

Modeling and simulation: tools for metabolic engineering.

Wolfgang Wiechert1.   

Abstract

Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently used metabolic modeling approaches can be subdivided into structural models, stoichiometric models, carbon flux models, stationary and nonstationary mechanistic models and models with gene regulation. However, the power of a model strongly depends on its basic modeling assumptions, the simplifications made and the data sources used. Model validation turns out to be particularly difficult for metabolic systems. The different modeling approaches are critically reviewed with respect to their potential and benefits for the metabolic engineering cycle. Several tools that have emerged from the different modeling approaches including structural pathway synthesis, stoichiometric pathway analysis, metabolic flux analysis, metabolic control analysis, optimization of regulatory architectures and the evaluation of rapid sampling experiments are discussed.

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Year:  2002        PMID: 11792451     DOI: 10.1016/s0168-1656(01)00418-7

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  15 in total

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2.  Reaction networks and evolutionary game theory.

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3.  Aeration effects on metabolic events during sporulation of Bacillus thuringiensis.

Authors:  Mohammad H Sarrafzadeh; Sabine Schorr-Galindo; Hyun-Joon La; Hee-Mock Oh
Journal:  J Microbiol       Date:  2014-06-28       Impact factor: 3.422

4.  Development of a kinetic metabolic model: application to Catharanthus roseus hairy root.

Authors:  M Leduc; C Tikhomiroff; M Cloutier; M Perrier; M Jolicoeur
Journal:  Bioprocess Biosyst Eng       Date:  2006-02-01       Impact factor: 3.210

5.  Short-term metabolome dynamics and carbon, electron, and ATP balances in chemostat-grown Saccharomyces cerevisiae CEN.PK 113-7D following a glucose pulse.

Authors:  Liang Wu; Jan van Dam; Dick Schipper; M T A Penia Kresnowati; Angela M Proell; Cor Ras; Wouter A van Winden; Walter M van Gulik; Joseph J Heijnen
Journal:  Appl Environ Microbiol       Date:  2006-05       Impact factor: 4.792

6.  Effects of the presence of ColE1 plasmid DNA in Escherichia coli on the host cell metabolism.

Authors:  Zhijun Wang; Li Xiang; Junjie Shao; Alicja Wegrzyn; Grzegorz Wegrzyn
Journal:  Microb Cell Fact       Date:  2006-11-17       Impact factor: 5.328

Review 7.  Macroscopic modeling of mammalian cell growth and metabolism.

Authors:  Bassem Ben Yahia; Laetitia Malphettes; Elmar Heinzle
Journal:  Appl Microbiol Biotechnol       Date:  2015-07-22       Impact factor: 4.813

8.  Predictive potential of flux balance analysis of Saccharomyces cerevisiae using as optimization function combinations of cell compartmental objectives.

Authors:  Carlos Eduardo García Sánchez; César Augusto Vargas García; Rodrigo Gonzalo Torres Sáez
Journal:  PLoS One       Date:  2012-08-09       Impact factor: 3.240

9.  Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering.

Authors:  Marjan De Mey; Jo Maertens; Gaspard J Lequeux; Wim K Soetaert; Erick J Vandamme
Journal:  BMC Biotechnol       Date:  2007-06-18       Impact factor: 2.563

10.  (13)C Tracers for Glucose Degrading Pathway Discrimination in Gluconobacter oxydans 621H.

Authors:  Steffen Ostermann; Janine Richhardt; Stephanie Bringer; Michael Bott; Wolfgang Wiechert; Marco Oldiges
Journal:  Metabolites       Date:  2015-09-02
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