Literature DB >> 10099334

Metabolic design: how to engineer a living cell to desired metabolite concentrations and fluxes.

B N Kholodenko1, M Cascante, J B Hoek, H V Westerhoff, J Schwaber.   

Abstract

A biotechnological aim of genetic engineering is to increase the intracellular concentration or secretion of valuable compounds, while making the other concentrations and fluxes optimal for viability and productivity. Efforts to accomplish this based on over-expression of the enzyme, catalyzing the so-called "rate-limiting step," have not been successful. Here we develop a method to determine the enzyme concentrations that are required to achieve such an aim. This method is called Metabolic Design Analysis and is based on the perturbation method and the modular ("top-down") approach-formalisms that were first developed for the analysis of biochemical regulation such as, Metabolic Control Analysis. Contrary to earlier methods, the desired alterations of cellular metabolism need not be small or confined to a single metabolite or flux. The limits to the alterations of fluxes and metabolite concentrations are identified. To employ Metabolic Design Analysis, only limited kinetic information concerning the pathway enzymes is needed. Copyright 1998 John Wiley & Sons, Inc.

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Year:  1998        PMID: 10099334     DOI: 10.1002/(sici)1097-0290(19980720)59:2<239::aid-bit11>3.0.co;2-9

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  10 in total

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7.  Mathematical analysis of the influence of brain metabolism on the BOLD signal in Alzheimer's disease.

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8.  DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems.

Authors:  Robert W Smith; Rik P van Rosmalen; Vitor A P Martins Dos Santos; Christian Fleck
Journal:  BMC Syst Biol       Date:  2018-06-19

9.  Ensemble modeling for aromatic production in Escherichia coli.

Authors:  Matthew L Rizk; James C Liao
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10.  Systematic construction of kinetic models from genome-scale metabolic networks.

Authors:  Natalie J Stanford; Timo Lubitz; Kieran Smallbone; Edda Klipp; Pedro Mendes; Wolfram Liebermeister
Journal:  PLoS One       Date:  2013-11-14       Impact factor: 3.240

  10 in total

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