Literature DB >> 23897661

Computational approaches for understanding energy metabolism.

Alexander A Shestov1, Brandon Barker, Zhenglong Gu, Jason W Locasale.   

Abstract

There has been a surge of interest in understanding the regulation of metabolic networks involved in disease in recent years. Quantitative models are increasingly being used to interrogate the metabolic pathways that are contained within this complex disease biology. At the core of this effort is the mathematical modeling of central carbon metabolism involving glycolysis and the citric acid cycle (referred to as energy metabolism). Here, we discuss several approaches used to quantitatively model metabolic pathways relating to energy metabolism and discuss their formalisms, successes, and limitations.
Copyright © 2013 Wiley Periodicals, Inc.

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Year:  2013        PMID: 23897661      PMCID: PMC3906216          DOI: 10.1002/wsbm.1238

Source DB:  PubMed          Journal:  Wiley Interdiscip Rev Syst Biol Med        ISSN: 1939-005X


  118 in total

1.  Regulation of gene expression in flux balance models of metabolism.

Authors:  M W Covert; C H Schilling; B Palsson
Journal:  J Theor Biol       Date:  2001-11-07       Impact factor: 2.691

2.  Metabolic isotopomer labeling systems. Part I: global dynamic behavior.

Authors:  W Wiechert; M Wurzel
Journal:  Math Biosci       Date:  2001-02       Impact factor: 2.144

3.  Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions.

Authors:  Maciek R Antoniewicz; Joanne K Kelleher; Gregory Stephanopoulos
Journal:  Metab Eng       Date:  2006-09-17       Impact factor: 9.783

4.  Fast thermodynamically constrained flux variability analysis.

Authors:  Arne C Müller; Alexander Bockmayr
Journal:  Bioinformatics       Date:  2013-02-06       Impact factor: 6.937

Review 5.  Metabolic network flux analysis for engineering plant systems.

Authors:  Yair Shachar-Hill
Journal:  Curr Opin Biotechnol       Date:  2013-02-08       Impact factor: 9.740

6.  A whole-cell computational model predicts phenotype from genotype.

Authors:  Jonathan R Karr; Jayodita C Sanghvi; Derek N Macklin; Miriam V Gutschow; Jared M Jacobs; Benjamin Bolival; Nacyra Assad-Garcia; John I Glass; Markus W Covert
Journal:  Cell       Date:  2012-07-20       Impact factor: 41.582

7.  A linear steady-state treatment of enzymatic chains. General properties, control and effector strength.

Authors:  R Heinrich; T A Rapoport
Journal:  Eur J Biochem       Date:  1974-02-15

8.  New insights into central roles of cerebral oxygen metabolism in the resting and stimulus-evoked brain.

Authors:  Xiao-Hong Zhu; Nanyin Zhang; Yi Zhang; Kâmil Uğurbil; Wei Chen
Journal:  J Cereb Blood Flow Metab       Date:  2008-09-10       Impact factor: 6.200

9.  Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology.

Authors:  Alexei Vazquez; Zoltán N Oltvai
Journal:  PLoS One       Date:  2011-04-29       Impact factor: 3.240

10.  Metabolic dynamics in skeletal muscle during acute reduction in blood flow and oxygen supply to mitochondria: in-silico studies using a multi-scale, top-down integrated model.

Authors:  Ranjan K Dash; Yanjun Li; Jaeyeon Kim; Daniel A Beard; Gerald M Saidel; Marco E Cabrera
Journal:  PLoS One       Date:  2008-09-09       Impact factor: 3.240

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  11 in total

Review 1.  Metabolomics: A Primer.

Authors:  Xiaojing Liu; Jason W Locasale
Journal:  Trends Biochem Sci       Date:  2017-02-11       Impact factor: 13.807

2.  A Flux Balance of Glucose Metabolism Clarifies the Requirements of the Warburg Effect.

Authors:  Ziwei Dai; Alexander A Shestov; Luhua Lai; Jason W Locasale
Journal:  Biophys J       Date:  2016-09-06       Impact factor: 4.033

3.  A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data.

Authors:  Narayanan Sadagopan; Yiping Wang; Brandon E Barker; Kieran Smallbone; Christopher R Myers; Hongwei Xi; Jason W Locasale; Zhenglong Gu
Journal:  Comput Biol Chem       Date:  2015-09-01       Impact factor: 2.877

Review 4.  Understanding metabolism with flux analysis: From theory to application.

Authors:  Ziwei Dai; Jason W Locasale
Journal:  Metab Eng       Date:  2016-09-22       Impact factor: 9.783

5.  Bonded Cumomer Analysis of Human Melanoma Metabolism Monitored by 13C NMR Spectroscopy of Perfused Tumor Cells.

Authors:  Alexander A Shestov; Anthony Mancuso; Seung-Cheol Lee; Lili Guo; David S Nelson; Jeffrey C Roman; Pierre-Gilles Henry; Dennis B Leeper; Ian A Blair; Jerry D Glickson
Journal:  J Biol Chem       Date:  2015-12-24       Impact factor: 5.157

6.  Mitochondrial Respiration Chain Enzymatic Activities in the Human Brain: Methodological Implications for Tissue Sampling and Storage.

Authors:  Marcelo Fernando Ronsoni; Aline Pertile Remor; Mark William Lopes; Alexandre Hohl; Iris H Z Troncoso; Rodrigo Bainy Leal; Gustavo Luchi Boos; Charles Kondageski; Jean Costa Nunes; Marcelo Neves Linhares; Kátia Lin; Alexandra Susana Latini; Roger Walz
Journal:  Neurochem Res       Date:  2015-11-19       Impact factor: 3.996

Review 7.  Redox control of glutamine utilization in cancer.

Authors:  L Alberghina; D Gaglio
Journal:  Cell Death Dis       Date:  2014-12-04       Impact factor: 8.469

Review 8.  Systems biology of host-microbe metabolomics.

Authors:  Almut Heinken; Ines Thiele
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2015-04-30

9.  Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step.

Authors:  Alexander A Shestov; Xiaojing Liu; Zheng Ser; Ahmad A Cluntun; Yin P Hung; Lei Huang; Dongsung Kim; Anne Le; Gary Yellen; John G Albeck; Jason W Locasale
Journal:  Elife       Date:  2014-07-09       Impact factor: 8.140

10.  (13)C MRS and LC-MS Flux Analysis of Tumor Intermediary Metabolism.

Authors:  Alexander A Shestov; Seung-Cheol Lee; Kavindra Nath; Lili Guo; David S Nelson; Jeffrey C Roman; Dennis B Leeper; Mariusz A Wasik; Ian A Blair; Jerry D Glickson
Journal:  Front Oncol       Date:  2016-06-15       Impact factor: 6.244

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