Literature DB >> 11875424

Metabolic control analysis in drug discovery and disease.

Marta Cascante1, Laszlo G Boros, Begoña Comin-Anduix, Pedro de Atauri, Josep J Centelles, Paul W-N Lee.   

Abstract

Metabolic control analysis (MCA) provides a quantitative description of substrate flux in response to changes in system parameters of complex enzyme systems. Medical applications of the approach include the following: understanding the threshold effect in the manifestation of metabolic diseases; investigating the gene dose effect of aneuploidy in inducing phenotypic transformation in cancer; correlating the contributions of individual genes and phenotypic characteristics in metabolic disease (e.g., diabetes); identifying candidate enzymes in pathways suitable as targets for cancer therapy; and elucidating the function of "silent" genes by identifying metabolic features shared with genes of known pathways. MCA complements current studies of genomics and proteomics, providing a link between biochemistry and functional genomics that relates the expression of genes and gene products to cellular biochemical and physiological events. Thus, it is an important tool for the study of genotype-phenotype correlations. It allows genes to be ranked according to their importance in controlling and regulating cellular metabolic networks. We can expect that MCA will have an increasing impact on the choice of targets for intervention in drug discovery.

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Year:  2002        PMID: 11875424     DOI: 10.1038/nbt0302-243

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  65 in total

1.  Metabolic control analysis under uncertainty: framework development and case studies.

Authors:  Liqing Wang; Inanç Birol; Vassily Hatzimanikatis
Journal:  Biophys J       Date:  2004-10-01       Impact factor: 4.033

Review 2.  Stable isotope-resolved metabolomics and applications for drug development.

Authors:  Teresa W-M Fan; Pawel K Lorkiewicz; Katherine Sellers; Hunter N B Moseley; Richard M Higashi; Andrew N Lane
Journal:  Pharmacol Ther       Date:  2011-12-23       Impact factor: 12.310

Review 3.  Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

Authors:  Goutham N Vemuri; Aristos A Aristidou
Journal:  Microbiol Mol Biol Rev       Date:  2005-06       Impact factor: 11.056

4.  Control over action potential, calcium peak and average fluxes in the cyclic quasi-steady-state ion transport system in cardiac myocytes: in silico studies.

Authors:  Jaroslaw Dzbek; Bernard Korzeniewski
Journal:  Biochem J       Date:  2007-06-01       Impact factor: 3.857

Review 5.  Intelligently deciphering unintelligible designs: algorithmic algebraic model checking in systems biology.

Authors:  Bud Mishra
Journal:  J R Soc Interface       Date:  2009-04-08       Impact factor: 4.118

6.  A new strategy for assessing sensitivities in biochemical models.

Authors:  Sven Sahle; Pedro Mendes; Stefan Hoops; Ursula Kummer
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2008-10-13       Impact factor: 4.226

7.  Long signaling cascades tend to attenuate retroactivity.

Authors:  Hamid R Ossareh; Alejandra C Ventura; Sofia D Merajver; Domitilla Del Vecchio
Journal:  Biophys J       Date:  2011-04-06       Impact factor: 4.033

8.  Amino Acids Rather than Glucose Account for the Majority of Cell Mass in Proliferating Mammalian Cells.

Authors:  Aaron M Hosios; Vivian C Hecht; Laura V Danai; Marc O Johnson; Jeffrey C Rathmell; Matthew L Steinhauser; Scott R Manalis; Matthew G Vander Heiden
Journal:  Dev Cell       Date:  2016-03-07       Impact factor: 12.270

9.  Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production.

Authors:  Seth B Roberts; Christopher M Gowen; J Paul Brooks; Stephen S Fong
Journal:  BMC Syst Biol       Date:  2010-03-22

10.  Metabolic investigation of host/pathogen interaction using MS2-infected Escherichia coli.

Authors:  Rishi Jain; Ranjan Srivastava
Journal:  BMC Syst Biol       Date:  2009-12-30
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