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EcoSal Plus

Domain 3:

Metabolism

Amino Acid Metabolism and Fluxes

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  • Author: G. Wesley Hatfield1
  • Editor: Valley Stewart2
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Microbiology and Molecular Genetics, University of California—Irvine, Irvine, CA 92697-4025; 2: University of California, Davis, Davis, CA
  • Received 20 March 2008 Accepted 29 May 2008 Published 20 August 2008
  • Address correspondence to G. Wesley Hatfield gwhatfie@uci.edu
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  • Abstract:

    By the mid1960s, the pioneering work of Umbarger and Gerhart and Pardee had shown us that carbon flow through a biosynthetic pathway was controlled by allosteric inhibition of the first enzyme of the pathway by its end product; and, studies of the operon by Jacob and Monod had established that genes were controlled by an operator-repressor mechanism. During the intervening forty-plus years, knowledge and technologies have continued to explode in unanticipated ways. Today, we understand in great detail the molecular mechanisms of the many levels of metabolic and genetic regulation that control carbon flow through the amino acid biosynthetic pathways. Traditional experimental approaches are not sufficient for the integration and reconstruction of complex biological systems using data mostly generated by high-throughput experiments. Only with computational methods and adequate modeling tools will we be able to reconstruct and query these large and complicated systems. Due to complicated enzyme reaction mechanisms and the frequent lack of rate constant measurements needed for solving differential equations, most investigators have turned their attention to the development of abstract, top-down modeling tools. For example, Palsson and colleagues have used metabolic flux balance analysis (FBA) methods to simulate steady-state metabolite flux through pathways representing hundreds of enzyme steps. Recently, Yang et al. have developed a bottom-up, enzyme mechanism modeling language, kMech (kinetic mechanism), for the mathematical simulation of metabolic pathways.

  • Citation: Hatfield G. 2008. Amino Acid Metabolism and Fluxes, EcoSal Plus 2008; doi:10.1128/ecosalplus.3.6.1

Key Concept Ranking

Branched-Chain Amino Acid Biosynthesis
0.45603585
0.45603585

References

1. Umbarger HE. 1961. Feedback control by endproduct inhibition. Cold Spring Harbor Symp Quant Biol 26:301–312.[PubMed]
2. Gerhart JC, Pardee AB. 1962. The enzymology of control by feedback inhibition. J Biol Chem 237:891–896.[PubMed]
3. Monod J, Wyman J, Changeaux JP. 1965. On the nature of allosteric transitions: a sensible model. J Mol Biol 12:88–118. [PubMed][CrossRef]
4. Jacob F, Monod J. 1961. Genetic regulatory mechanisms in the synthesis of proteins. J Mol Biol 3:318–356.
5. Reed JL, Palsson BO. 2003. Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 185:2692–2699. [PubMed][CrossRef]
6. Alves R, Savageau MA. 2000. Comparing systemic properties of ensembles of biological networks by graphical and statistical methods. Bioinformatics 16:527–533. [PubMed][CrossRef]
7. Hatzimanikatis V, Bailey JE. 1996. MCA has more to say. J Theor Biol 182:233–242. [PubMed][CrossRef]
8. Najdi TS, Yang C-R, Shapiro BE, Hatfield GW, Mjolsness ED. 2006. Application of a generalized MWC model for the mathematical simulation of metabolic pathways regulated by allosteric enzymes. J Bioinform Comput Biol 4:335. [CrossRef]
9. Yang C-R, Shapiro B, Mjolsness E, Hatfield GW. 2005. An enzyme mechanism language for mathematical modeling of metabolic pathways. Bioinformatics 21:774–780. [PubMed][CrossRef]
10. Yang CR, Shapiro BE, Hung SP, Mjolsness ED, Hatfield GWA. 2005. Mathematical model for the biosynthesis of the branched chain amino acids in Escherichia coli K12. J Biol Chem 280:11224–11232. [PubMed][CrossRef]
11. Shapiro BE, Levchenko A, Meyerowitz EM, Wold BJ, Mjolsness ED. 2003. Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations. Bioinformatics 19:677–678. [PubMed][CrossRef]
12. Najdi T. 2008. Ph.D. dissertation. University of California, Irvine.
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/content/journal/ecosalplus/10.1128/ecosalplus.3.6.1
2008-08-20
2017-09-24

Abstract:

By the mid1960s, the pioneering work of Umbarger and Gerhart and Pardee had shown us that carbon flow through a biosynthetic pathway was controlled by allosteric inhibition of the first enzyme of the pathway by its end product; and, studies of the operon by Jacob and Monod had established that genes were controlled by an operator-repressor mechanism. During the intervening forty-plus years, knowledge and technologies have continued to explode in unanticipated ways. Today, we understand in great detail the molecular mechanisms of the many levels of metabolic and genetic regulation that control carbon flow through the amino acid biosynthetic pathways. Traditional experimental approaches are not sufficient for the integration and reconstruction of complex biological systems using data mostly generated by high-throughput experiments. Only with computational methods and adequate modeling tools will we be able to reconstruct and query these large and complicated systems. Due to complicated enzyme reaction mechanisms and the frequent lack of rate constant measurements needed for solving differential equations, most investigators have turned their attention to the development of abstract, top-down modeling tools. For example, Palsson and colleagues have used metabolic flux balance analysis (FBA) methods to simulate steady-state metabolite flux through pathways representing hundreds of enzyme steps. Recently, Yang et al. have developed a bottom-up, enzyme mechanism modeling language, kMech (kinetic mechanism), for the mathematical simulation of metabolic pathways.

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