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Metabolites
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Metabolic Flux Analysis
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Metabolic Flux Analysis

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 August 2015) | Viewed by 48977

Special Issue Editor

Prof. Dr. Kazuyuki Shimizu

E-Mail Website
Guest Editor
Institute of advanced biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
Interests: 13C-etabolic flux analysis; metabolic regulation analysis; systems biology in particular modeling

Special Issue Information

Dear Colleagues,

Metabolic fluxes are the most important phenotype, and are the end result of the interplay of gene expression, protein expression, enzyme kinetics, and metabolite concentrations. A quantitative representation of this phenotype can be estimated with metabolic flux analysis (MFA), based on either flux balance analysis (FBA), which is obtained via stoichiometric constraints, or 13C-metabolic flux analysis (13C-MFA), which is obtained via stable isotope balances, together with stoichiometric constraints. Accurate information concerning metabolic fluxes is important for both understanding metabolic regulation mechanisms (science) and metabolic engineering applications (engineering).

This Special Issue of Metabolites, “Metabolic Flux Analysis”, will be dedicated not only to the development of the flux analysis method, but also to applications enabling an in-depth understanding of metabolisms or metabolic regulation mechanisms, and of metabolic engineering applications or fermentation improvements (which are achieved through the optimization of culture conditions). Organisms covered include microorganisms (such as E.coli, Yeast, Bacillus, Corynebacteria, Streptomyces, etc.), photosynthetic organisms (such as plant, algae, and cyanobacteria), and mammalian cells (such as mouse, human, and cancer cells). Moreover, this Special Issue also covers systems biology approaches, such as FBA-based modeling (and its extension into genome-scale metabolic modeling) and kinetic modeling.

Prof. Dr. Kazuyuki Shimizu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolic fluxes
  • metabolites
  • metabolic regulation
  • integration of omics data
  • flux balance analysis
  • kinetic modeling
  • genome-scale modeling
  • metabolic engineering
  • fermentation
  • synthetic biology

Published Papers (7 papers)

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Research

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318 KiB  
Article
Flux Balance Analysis Inspired Bioprocess Upgrading for Lycopene Production by a Metabolically Engineered Strain of Yarrowia lipolytica
by Komi Nambou, Xingxing Jian, Xinkai Zhang, Liujing Wei, Jiajia Lou, Catherine Madzak and Qiang Hua
Metabolites 2015, 5(4), 794-813; https://doi.org/10.3390/metabo5040794 - 21 Dec 2015
Cited by 27 | Viewed by 5892
Abstract
Genome-scale metabolic models embody a significant advantage of systems biology since their applications as metabolic flux simulation models enable predictions for the production of industrially-interesting metabolites. The biotechnological production of lycopene from Yarrowia lipolytica is an emerging scope that has not been fully [...] Read more.
Genome-scale metabolic models embody a significant advantage of systems biology since their applications as metabolic flux simulation models enable predictions for the production of industrially-interesting metabolites. The biotechnological production of lycopene from Yarrowia lipolytica is an emerging scope that has not been fully scrutinized, especially for what concerns cultivation conditions of newly generated engineered strains. In this study, by combining flux balance analysis (FBA) and Plackett-Burman design, we screened chemicals for lycopene production from a metabolically engineered strain of Y. lipolytica. Lycopene concentrations of 126 and 242 mg/L were achieved correspondingly from the FBA-independent and the FBA-assisted designed media in fed-batch cultivation mode. Transcriptional studies revealed upregulations of heterologous genes in media designed according to FBA, thus implying the efficiency of model predictions. Our study will potentially support upgraded lycopene and other terpenoids production from existing or prospect bioengineered strains of Y. lipolytica and/or closely related yeast species. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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3475 KiB  
Article
Quasi-Steady-State Analysis based on Structural Modules and Timed Petri Net Predict System’s Dynamics: The Life Cycle of the Insulin Receptor
by Jennifer Scheidel, Klaus Lindauer, Jörg Ackermann and Ina Koch
Metabolites 2015, 5(4), 766-793; https://doi.org/10.3390/metabo5040766 - 17 Dec 2015
Cited by 9 | Viewed by 6825
Abstract
The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as [...] Read more.
The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as theoretically. We developed a time-resolved, discrete model to describe stochastic dynamics and study the approximation of non-linear dynamics in the context of timed Petri nets. Additionally, using a graph-theoretical approach, we analyzed the structure of the regulatory system and demonstrated the close interrelation of structural network properties with the kinetic behavior. The transition invariants decomposed the model into overlapping subnetworks of various sizes, which represent basic functional modules. Moreover, we computed the quasi-steady states of these subnetworks and demonstrated that they are fundamental to understand the dynamic behavior of the system. The Petri net approach confirms the experimental results of insulin-stimulated degradation of the insulin receptor, which represents a common feature of insulin-resistant, hyperinsulinaemic states. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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757 KiB  
Article
Effective Estimation of Dynamic Metabolic Fluxes Using 13C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability
by Robin Schumacher and S. Aljoscha Wahl
Metabolites 2015, 5(4), 697-719; https://doi.org/10.3390/metabo5040697 - 04 Dec 2015
Cited by 10 | Viewed by 5398
Abstract
The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and [...] Read more.
The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and 13C labeling to identify piecewise-affine (PWA) flux functions has been described recently. Obtaining flux estimates nevertheless still required frequent manual reinitalization to obtain a good reproduction of the experimental data and, moreover, did not optimize on all observables simultaneously (metabolites and isotopomer concentrations). In our contribution we focus on measures to achieve faster and robust dynamic flux estimation which leads to a high dimensional parameter estimation problem. Specifically, we address the following challenges within the PWA problem formulation: (1) Fast selection of sufficient domains for the PWA flux functions, (2) Control of over-fitting in the concentration space using shape-prescriptive modeling and (3) robust and efficient implementation of the parameter estimation using the hybrid implicit filtering algorithm. With the improvements we significantly speed up the convergence by efficiently exploiting that the optimization problem is partly linear. This allows application to larger-scale metabolic networks and demonstrates that the proposed approach is not purely theoretical, but also applicable in practice. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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1526 KiB  
Article
Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow
by Christiana Sehr, Andreas Kremling and Alberto Marin-Sanguino
Metabolites 2015, 5(4), 601-635; https://doi.org/10.3390/metabo5040601 - 16 Oct 2015
Cited by 4 | Viewed by 5454
Abstract
During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it [...] Read more.
During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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1103 KiB  
Article
13C Tracers for Glucose Degrading Pathway Discrimination in Gluconobacter oxydans 621H
by Steffen Ostermann, Janine Richhardt, Stephanie Bringer, Michael Bott, Wolfgang Wiechert and Marco Oldiges
Metabolites 2015, 5(3), 455-474; https://doi.org/10.3390/metabo5030455 - 02 Sep 2015
Viewed by 6084
Abstract
Gluconobacter oxydans 621H is used as an industrial production organism due to its exceptional ability to incompletely oxidize a great variety of carbohydrates in the periplasm. With glucose as the carbon source, up to 90% of the initial concentration is oxidized periplasmatically to [...] Read more.
Gluconobacter oxydans 621H is used as an industrial production organism due to its exceptional ability to incompletely oxidize a great variety of carbohydrates in the periplasm. With glucose as the carbon source, up to 90% of the initial concentration is oxidized periplasmatically to gluconate and ketogluconates. Growth on glucose is biphasic and intracellular sugar catabolism proceeds via the Entner–Doudoroff pathway (EDP) and the pentose phosphate pathway (PPP). Here we studied the in vivo contributions of the two pathways to glucose catabolism on a microtiter scale. In our approach we applied specifically 13C labeled glucose, whereby a labeling pattern in alanine was generated intracellularly. This method revealed a dynamic growth phase-dependent pathway activity with increased activity of EDP in the first and PPP in the second growth phase, respectively. Evidence for a growth phase-independent decarboxylation-carboxylation cycle around the pyruvate node was obtained from 13C fragmentation patterns of alanine. For the first time, down-scaled microtiter plate cultivation together with 13C-labeled substrate was applied for G. oxydans to elucidate pathway operation, exhibiting reasonable labeling costs and allowing for sufficient replicate experiments. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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Review

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419 KiB  
Review
Cancer Metabolism and Drug Resistance
by Mahbuba Rahman and Mohammad Rubayet Hasan
Metabolites 2015, 5(4), 571-600; https://doi.org/10.3390/metabo5040571 - 30 Sep 2015
Cited by 119 | Viewed by 11870
Abstract
Metabolic alterations, driven by genetic and epigenetic factors, have long been known to be associated with the etiology of cancer. Furthermore, accumulating evidence suggest that cancer metabolism is intimately linked to drug resistance, which is currently one of the most important challenges in [...] Read more.
Metabolic alterations, driven by genetic and epigenetic factors, have long been known to be associated with the etiology of cancer. Furthermore, accumulating evidence suggest that cancer metabolism is intimately linked to drug resistance, which is currently one of the most important challenges in cancer treatment. Altered metabolic pathways help cancer cells to proliferate at a rate higher than normal, adapt to nutrient limited conditions, and develop drug resistance phenotypes. Application of systems biology, boosted by recent advancement of novel high-throughput technologies to obtain cancer-associated, transcriptomic, proteomic and metabolomic data, is expected to make a significant contribution to our understanding of metabolic properties related to malignancy. Indeed, despite being at a very early stage, quantitative data obtained from the omics platforms and through applications of 13C metabolic flux analysis (MFA) in in vitro studies, researchers have already began to gain insight into the complex metabolic mechanisms of cancer, paving the way for selection of molecular targets for therapeutic interventions. In this review, we discuss some of the major findings associated with the metabolic pathways in cancer cells and also discuss new evidences and achievements on specific metabolic enzyme targets and target-directed small molecules that can potentially be used as anti-cancer drugs. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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178 KiB  
Review
Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations
by Saratram Gopalakrishnan and Costas D. Maranas
Metabolites 2015, 5(3), 521-535; https://doi.org/10.3390/metabo5030521 - 18 Sep 2015
Cited by 14 | Viewed by 6463
Abstract
Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements [...] Read more.
Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
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