Special Issue "Metabolic Network Models Volume 2"

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (31 March 2018).

Special Issue Editors

Dr. Zoran Nikoloski
Website
Guest Editor
Max Planck Institute of Molecular Plant Physiology and University of Potsdam, Germany
Interests: constraint-based modeling, large-scale data integration, robustness and plasticity of phenotypes, genotype-phenotype mapping, evolutionary metabolomics
Dr. Georg Basler
Website
Guest Editor
Max Planck Institute for Molecular Plant Physiology, Germany
Interests: computational biology, algorithm development, metabolic modeling, plant-microbe interactions, metabolic engineering, systems biology, retrosynthesis, synthetic biology, network analysis

Special Issue Information

Dear Colleagues,

The last two decades of systems biology research have propelled the construction of large-scale models of metabolism in diverse organisms across all kingdoms of life. These models formalize the description of the entirety of characterized biochemical reactions in a biological system across different organizational scales (e.g., pathways, organelles, cell types, and interconnected organs). The development of these medium- and large-scale metabolic model backbones have been coupled with development of computational approaches that allow for inferring genotype–phenotype relationships, characterizing the effect of particular environments and cellular contexts on the traits of interest. These methodological advances go hand-in-hand with headway in technologies for high-throughput profiling of cellular components. Altogether, integrative data-driven large-scale modeling has provide the possibility to posit hypotheses about metabolic functions and to test their validity in particular scenarios.

Yet, despite these advances, there is still the need to develop methods that can provide testable predictions by integrating large-scale models with measurable read-outs (e.g., protein abundance, metabolite content). These methods should allow for augmenting the structural backbone of the metabolic models with additional information about: (i) the type of kinetic law governing the reaction rates, (ii) constraints concerning feasible parameter values for the respective kinetic, and (iii) thermodynamic feasibility of reactions. They should also be coupled with appropriate approaches for model selection based on steady-state and time-resolved data from multiple experimental scenarios. Therefore, this Special Issue of Metabolites will be dedicated to publishing current advances on medium- and large-scale metabolic network modeling that address these pressing challenges by providing case studies of hypotheses that can be tested by integration of data from modern metabolomics technologies.

Dr. Zoran Nikoloski
Dr. Georg Basler
Guest Editors

Manuscript Submission Information

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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 1600 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 network
  • constraint-based modeling
  • reaction kinetic
  • thermodynamic constraints
  • model selection
  • multi-view data
  • regulation of reaction rates
  • metabolomics data
  • network robustness and plasticity

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Published Papers (2 papers)

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Research

Open AccessArticle
Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis
Metabolites 2018, 8(1), 3; https://doi.org/10.3390/metabo8010003 - 04 Jan 2018
Cited by 2
Abstract
Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- [...] Read more.
Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore. Full article
(This article belongs to the Special Issue Metabolic Network Models Volume 2)
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Open AccessArticle
Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca
Metabolites 2017, 7(4), 57; https://doi.org/10.3390/metabo7040057 - 11 Nov 2017
Cited by 5
Abstract
Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given [...] Read more.
Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism. Full article
(This article belongs to the Special Issue Metabolic Network Models Volume 2)
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