Biotechnology Journal

Cover image for Vol. 8 Issue 9

Special Issue: Metabolic Modeling and Simulation

September 2013

Volume 8, Issue 9

Pages 869–1116

  1. Cover Picture

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    4. Editorial
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    6. Contents
    7. BiotecVisions
    8. Forum
    9. Reviews
    10. Mini-Review
    11. Review
    12. Technical Report
    13. Research Articles
    14. Meetings
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      Metabolic Modeling and Simulation

      Version of Record online: 9 SEP 2013 | DOI: 10.1002/biot.201390044

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      Special issue: Metabolic Modeling and Simulation. Modeling of cellular metabolism has been a major area of research for bioengineers and biomedical researchers alike. This Special Issue collects a series of articles on methods of metabolic modeling, modeling of human metabolism, modeling of microbial metabolism and modeling of bioprocesses. This cover is a visual representation of the essence of metabolic engineering. Image: © rolffimages – Fotolia.com.

  2. Editorial Board

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    4. Editorial
    5. In this issue
    6. Contents
    7. BiotecVisions
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    9. Reviews
    10. Mini-Review
    11. Review
    12. Technical Report
    13. Research Articles
    14. Meetings
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  3. Editorial

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      Editorial: Metabolic modeling in biotechnology and medical research (pages 962–963)

      Prof. Diethard Mattanovich and Prof. Vassily Hatzimanikatis

      Version of Record online: 9 SEP 2013 | DOI: 10.1002/biot.201300378

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      Metabolic Modeling and Simulation: This special issue of Biotechnology Journal is edited by Diethard Mattanovich and Vassily Hatzimanikatis and covers the state-of-the-art in metabolic modeling, including the major themes of methods in metabolic modeling, modeling of human and microbial metabolism, and modeling of bioprocesses.

  4. In this issue

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    2. Cover Picture
    3. Editorial Board
    4. Editorial
    5. In this issue
    6. Contents
    7. BiotecVisions
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    9. Reviews
    10. Mini-Review
    11. Review
    12. Technical Report
    13. Research Articles
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      In this issue (page 964)

      Version of Record online: 9 SEP 2013 | DOI: 10.1002/biot.201390045

  5. Contents

    1. Top of page
    2. Cover Picture
    3. Editorial Board
    4. Editorial
    5. In this issue
    6. Contents
    7. BiotecVisions
    8. Forum
    9. Reviews
    10. Mini-Review
    11. Review
    12. Technical Report
    13. Research Articles
    14. Meetings
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  6. BiotecVisions

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    2. Cover Picture
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    4. Editorial
    5. In this issue
    6. Contents
    7. BiotecVisions
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    9. Reviews
    10. Mini-Review
    11. Review
    12. Technical Report
    13. Research Articles
    14. Meetings
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  7. Forum

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      Improving biotechnology communication (pages 970–972)

      Marc-Denis Weitze and Alfred Pühler

      Version of Record online: 26 JUN 2013 | DOI: 10.1002/biot.201300182

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      Successful dialog between science and the public is vital for the development and introduction of new technologies. The National Academy of Science and Engineering in Germany has analysed experiences gained from controversies and communication strategies surrounding green genetic engineering and other fields of biotechnology, from a communications and social science viewpoint, as well as a historical perspective. From this, recommendations on how to communicate biotechnology in the future, with objectivity and balance, have been derived.

  8. Reviews

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    1. Multi-scale modeling for sustainable chemical production (pages 973–984)

      Dr. Kai Zhuang, Bhavik R. Bakshi and Dr. Markus J. Herrgård

      Version of Record online: 21 MAR 2013 | DOI: 10.1002/biot.201200272

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      Multi-scale modeling of sustainable chemical production – the authors review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, the authors propose a multi-scale approach for integrating the existing models into a cohesive framework.

    2. Genome-scale modeling of human metabolism – a systems biology approach (pages 985–996)

      Adil Mardinoglu, Francesco Gatto and Prof. Jens Nielsen

      Version of Record online: 24 APR 2013 | DOI: 10.1002/biot.201200275

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      Genome-scale metabolic models in the Human Metabolic Atlas facilitates the understanding of molecular mechanisms behind the etiology of metabolic diseases, with the goal of identifying novel biomarkers and therapeutic targets. These models can be used for simulation of whole-body metabolic functions using flux balance analysis and contribute to the development of personalized and translational medicine.

    3. You have full text access to this OnlineOpen article
      Basic concepts and principles of stoichiometric modeling of metabolic networks (pages 997–1008)

      Timo R. Maarleveld, Ruchir A. Khandelwal, Brett G. Olivier, Bas Teusink and Prof. Frank J. Bruggeman

      Version of Record online: 29 JUL 2013 | DOI: 10.1002/biot.201200291

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      Metabolism is responsible for the supply of energy and building blocks for cell growth and maintenance. One way to explore metabolism is by using computational modeling approaches. In this review, the authors use simple metabolic networks to explain the state-of-the-art computational modeling techniques. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces are explained with simple, illustrative examples. This review will provide rigorous and quantitative hypotheses and fundamental understanding for metabolic networks studies.

  9. Mini-Review

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      Elementary flux modes in a nutshell: Properties, calculation and applications (pages 1009–1016)

      Dr. Jürgen Zanghellini, David E. Ruckerbauer, Michael Hanscho and Christian Jungreuthmayer

      Version of Record online: 21 JUN 2013 | DOI: 10.1002/biot.201200269

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      Elementary flux mode (EFM) analysis is an ideal tool for pathway analysis with many applications in basic research and applied biotechnology, like metabolic engineering. In this review, the authors give a brief, user-oriented introduction to EFM analysis (EFMA), highlight recent applications, analyze current research strategies to overcome the computational restrictions and give an overview over current approaches, which tend to identify and calculate only biologically relevant EFMs.

  10. Review

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      Predicting complex phenotype–genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory (pages 1017–1034)

      Duygu Dikicioglu, Pınar Pir and Prof. Stephen G. Oliver

      Version of Record online: 23 AUG 2013 | DOI: 10.1002/biot.201300138

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      An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken towards improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

  11. Technical Report

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    1. Flux-coupled genes and their use in metabolic flux analysis (pages 1035–1042)

      Hyun Uk Kim, Won Jun Kim and Prof. Sang Yup Lee

      Version of Record online: 21 MAR 2013 | DOI: 10.1002/biot.201200279

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      Expression levels of flux-coupled genes (FCGs) provide additional information for the constraints-based flux analysis: hypothesizing that there exist genes whose expression levels correlate with their respective flux values across different conditions, the authors identified seven FCGs from the transcriptome and 13C-flux data of Escherichia coli. Constraints-based flux analysis with the FCGs captured greater number of correct flux changes than conventionally used methods, FBA and MOMA. Because transcriptional information on genes is routinely obtainable, this approach using FCGs will be useful in accurately determining flux values under varying conditions.

  12. Research Articles

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    1. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints (pages 1043–1057)

      Anirikh Chakrabarti, Ljubisa Miskovic, Keng Cher Soh and Prof. Vassily Hatzimanikatis

      Version of Record online: 20 AUG 2013 | DOI: 10.1002/biot.201300091

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      Using mass action kinetics in modeling metabolic processes can lead to overly conservative assessments of feasible physiological conditions of the organism. This study shows that considering the enzyme saturations as observed in biological systems consistently increases the stability of the kinetic models. This indicates that enzymes have evolved in a way to increase the flexibility and thus the viability and adaptability of the living organism. Thus, for consistent modeling of cellular metabolism enzyme saturation should be considered as an inevitable component.

    2. Metabolic gradients as key regulators in zonation of tumor energy metabolism: A tissue-scale model-based study (pages 1058–1069)

      Matthias König, Hermann-Georg Holzhütter and Nikolaus Berndt

      Version of Record online: 26 JUN 2013 | DOI: 10.1002/biot.201200393

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      A hallmark of many tumor types is the reprogramming of energy metabolism. In this work, the authors use a tissue-scale model of the two main ATP delivering pathways (glycolysis and oxidative phosphorylation) to analyze the effect of various strategies of tumor energy metabolism and the role of oxygen in the zonation of tumor metabolism.They conclude that metabolite availability functions as a key regulator of tumor energy metabolism, and this study could support the development of new anti-tumor drugs.

    3. Genomically and biochemically accurate metabolic reconstruction of Methanosarcina barkeri Fusaro, iMG746 (pages 1070–1079)

      Matthew C. Gonnerman, Matthew N. Benedict, Adam M. Feist, William W. Metcalf and Dr. Nathan D. Price

      Version of Record online: 26 MAR 2013 | DOI: 10.1002/biot.201200266

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      Improving the accuracy and scope of a Methanosarcina barkeri metabolic model: M. barkeri is a single-celled organism that naturally produces methane, the main ingredient of natural gas, as a byproduct of its metabolism. A metabolic model of methanogenesis exists, but many experiments on methanogenesis have since been done which have greatly furthered our understanding of this critical process. In this article, the authors update and validate the model to reflect this new data and use the new model to predict regulatory mechanisms for certain key metabolic enzymes.

    4. Kinetic isotope effects significantly influence intracellular metabolite 13C labeling patterns and flux determination (pages 1080–1089)

      Thomas M. Wasylenko and Prof. Gregory Stephanopoulos

      Version of Record online: 5 AUG 2013 | DOI: 10.1002/biot.201200276

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      Modeling of isotopic fractionation at the pyruvate node reveals that kinetic isotope effects significantly influence the distribution of 13C atoms in intracellular metabolites. Quantification of the modeling errors predicted to result from neglecting these effects in flux estimation algorithms shows that under some conditions the modeling errors can be comparable in size to measurement errors associated with mass spectrometry. Consequently, isotope effects must be considered as a source of error when assessing the statistical significance of results from 13C-Metabolic Flux Analysis studies.

    5. Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks (pages 1090–1104)

      Ali R. Zomorrodi, Jimmy G. Lafontaine Rivera, James C. Liao and Prof. Costas D. Maranas

      Version of Record online: 10 JUN 2013 | DOI: 10.1002/biot.201200270

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      The ensemble modeling (EM) procedure relies on screening models in the ensemble by imposing genetic perturbations, which result in new steady-state flux distributions compared to the reference strain. In this study, the authors propose an optimization-based framework using a genetic algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of screening. This pipeline can greatly improve the efficiency of the EM procedure by enabling faster convergence to a final set of physiologically relevant kinetic models, while minimizing the required experimental effort.

    6. Metabolic costs of amino acid and protein production in Escherichia coli (pages 1105–1114)

      Jun.-Prof. Dr. Christoph Kaleta, Sascha Schäuble, Ursula Rinas and Stefan Schuster

      Version of Record online: 15 JUL 2013 | DOI: 10.1002/biot.201200267

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      Escherichia coli is widely used in metabolic engineering for biosynthesis of major bio-compounds such as amino acids. In this study, the authors determine the metabolic cost associated with the production of amino acids and proteins on different growth media using several methods. They show that both methionine and leucine are very expensive amino acids. Moreover, the biosynthesis of amino acids from glucose and glycerol can be used to balance the high energetic cost of amino acid polymerization. The findings can significantly improve the future choice of such growth media with respect to energy efficiency and biosynthesis yield.

  13. Meetings

    1. Top of page
    2. Cover Picture
    3. Editorial Board
    4. Editorial
    5. In this issue
    6. Contents
    7. BiotecVisions
    8. Forum
    9. Reviews
    10. Mini-Review
    11. Review
    12. Technical Report
    13. Research Articles
    14. Meetings
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