Literature DB >> 8626472

Application of biochemical systems theory to metabolism in human red blood cells. Signal propagation and accuracy of representation.

T C Ni1, M A Savageau.   

Abstract

Human erythrocytes are among the simplest of cells. Many of their enzymes have been characterized kinetically using steady-state methods in vitro, and several investigators have assembled this kinetic information into mathematical models of the integrated system. However, despite its relative simplicity, the integrated behavior of erythrocyte metabolism is still complex and not well understood. Errors will inevitably be encountered in any such model because of this complexity; thus, the construction of an integrative model must be considered an iterative process of assessment and refinement. In a previous study, we selected a recent model of erythrocyte metabolism as our starting point and took it through three stages of model assessment and refinement using systematic strategies provided by biochemical systems theory. At each stage deficiencies were diagnosed, putative remedies were identified, and modifications consistent with existing experimental evidence were incorporated into the working model. In this paper we address two issues: the propagation of biochemical signals within the metabolic network, and the accuracy of kinetic representation. The analysis of signal propagation reveals the importance of glutathione peroxidase, transaldolase, and the concentration of total glutathione in determining systemic behavior. It also reveals a highly amplified diversion of flux between the pathways of pentose phosphate and nucleotide metabolism. In determining the range of accurate representation based on alternative kinetic formalisms we discovered large discrepancies. These were identified with the behavior of the model represented within the Michaelis-Menten formalism. This model fails to exhibit a nominal steady state when the activity of glutathione peroxidase is decreased by as little as 9%. Our current understanding, as embodied in this working model, is in need of further refinement, and the results presented in this paper suggest areas of the model where such effort might profitably be concentrated.

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Year:  1996        PMID: 8626472     DOI: 10.1074/jbc.271.14.7927

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  13 in total

Review 1.  Oxidative stress, inflammation and carcinogenesis are controlled through the pentose phosphate pathway by transaldolase.

Authors:  Andras Perl; Robert Hanczko; Tiffany Telarico; Zachary Oaks; Steve Landas
Journal:  Trends Mol Med       Date:  2011-03-02       Impact factor: 11.951

Review 2.  New views on the selection acting on genetic polymorphism in central metabolic genes.

Authors:  Walter F Eanes
Journal:  Ann N Y Acad Sci       Date:  2016-11-10       Impact factor: 5.691

3.  Deletion of Ser-171 causes inactivation, proteasome-mediated degradation and complete deficiency of human transaldolase.

Authors:  Craig E Grossman; Brian Niland; Christina Stancato; Nanda M Verhoeven; Marjo S Van Der Knaap; Cornelis Jakobs; Lawrence M Brown; Sandor Vajda; Katalin Banki; Andras Perl
Journal:  Biochem J       Date:  2004-09-01       Impact factor: 3.857

4.  Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition.

Authors:  Yoichi Nakayama; Ayako Kinoshita; Masaru Tomita
Journal:  Theor Biol Med Model       Date:  2005-05-09       Impact factor: 2.432

5.  Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.

Authors:  Osbaldo Resendis-Antonio
Journal:  PLoS One       Date:  2009-03-23       Impact factor: 3.240

6.  On the adaptive design rules of biochemical networks in evolution.

Authors:  Bor-Sen Chen; Wan-Shian Wu; Wei-Sheng Wu; Wen-Hsiung Li
Journal:  Evol Bioinform Online       Date:  2007-02-28       Impact factor: 1.625

7.  GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes.

Authors:  Kazuharu Arakawa; Yohei Yamada; Kosaku Shinoda; Yoichi Nakayama; Masaru Tomita
Journal:  BMC Bioinformatics       Date:  2006-03-23       Impact factor: 3.169

8.  A Three Stage Integrative Pathway Search (TIPS) framework to identify toxicity relevant genes and pathways.

Authors:  Zheng Li; Shireesh Srivastava; Sheenu Mittal; Xuerui Yang; Lufang Sheng; Christina Chan
Journal:  BMC Bioinformatics       Date:  2007-06-14       Impact factor: 3.169

9.  The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks.

Authors:  Sergio Grimbs; Joachim Selbig; Sascha Bulik; Hermann-Georg Holzhütter; Ralf Steuer
Journal:  Mol Syst Biol       Date:  2007-11-13       Impact factor: 11.429

10.  Elevation of mitochondrial transmembrane potential and reactive oxygen intermediate levels are early events and occur independently from activation of caspases in Fas signaling.

Authors:  K Banki; E Hutter; N J Gonchoroff; A Perl
Journal:  J Immunol       Date:  1999-02-01       Impact factor: 5.422

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