Literature DB >> 1737767

Systems analysis of the tricarboxylic acid cycle in Dictyostelium discoideum. II. Control analysis.

K R Albe1, B E Wright.   

Abstract

A steady-state computer model of the tricarboxylic cycle in Dictyostelium discoideum was analyzed using metabolic control theory. The steady state had variations of less than 0.04% over the last half of the simulation for both metabolite concentrations and fluxes. Metabolite and flux control coefficients were determined by varying enzymatic activities within 2% of their initial values and simulating the responses of metabolite concentrations and fluxes to these changes. Under these conditions, summation properties were met for most metabolite and all flux control coefficients. Maximum flux control coefficients were found for succinate dehydrogenase (0.35), malic enzyme (0.24), and malate dehydrogenase (-0.18). Comparable control was found for the reaction supplying pyruvate (0.14) and for the sum of the input amino acids (0.43), which serve as an energy source for D. discoideum. The time-dependent processes by which a new steady state was established were examined after increasing malic enzyme or malate dehydrogenase activities. This provided a method for an analysis of the mechanisms by which the observed control coefficients were generated. In addition, the effects of increasing the stimuli within 5-20% of the original enzyme activity were examined. Under these conditions, more typical of experimental stimuli and measurable responses, the metabolic model failed to return to steady state, and thus summation properties were not met. Whether "true" steady states ever occur or whether metabolic control theory can be applied in vivo is discussed.

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Year:  1992        PMID: 1737767

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


  7 in total

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Authors:  B Bost; C Dillmann; D de Vienne
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2.  Metabolic control analysis as a mechanism that conserves genetic variance during advanced cycle breeding.

Authors:  J Yu; R Bernardo
Journal:  Theor Appl Genet       Date:  2004-02-12       Impact factor: 5.699

3.  A comparative assessment of mandible shape in a consomic strain panel of the house mouse (Mus musculus)--implications for epistasis and evolvability of quantitative traits.

Authors:  Louis Boell; Sona Gregorova; Jiri Forejt; Diethard Tautz
Journal:  BMC Evol Biol       Date:  2011-10-19       Impact factor: 3.260

4.  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 5.  Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

Authors:  Fei He; Ettore Murabito; Hans V Westerhoff
Journal:  J R Soc Interface       Date:  2016-04-13       Impact factor: 4.118

6.  Flux prediction using artificial neural network (ANN) for the upper part of glycolysis.

Authors:  Anamya Ajjolli Nagaraja; Nicolas Fontaine; Mathieu Delsaut; Philippe Charton; Cedric Damour; Bernard Offmann; Brigitte Grondin-Perez; Frederic Cadet
Journal:  PLoS One       Date:  2019-05-08       Impact factor: 3.240

7.  Multilocus epistasis, linkage, and genetic variance in breeding populations with few parents.

Authors:  D A Tabanao; J Yu; R Bernardo
Journal:  Theor Appl Genet       Date:  2007-06-12       Impact factor: 5.574

  7 in total

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