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Metabolites
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Genome-Scale Metabolic Models
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Genome-Scale Metabolic Models

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

Deadline for manuscript submissions: closed (15 June 2021)

Special Issue Editor

Dr. Mark M.G. Poolman

E-Mail Website
Guest Editor
Biology and Medical Science, Oxford Brookes University, Oxford, UK
Interests: metabolic modeling; microbiology; biotechnology

Special Issue Information

Dear Colleagues,

Genome-scale metabolic models attempt to provide a representation of all biochemical reactions available to a given organism in a computer readable form. For the most part, such models are analyzed using techniques based on the fields of linear algebra and linear programming. In addition to providing a platform from which to investigate fundamental structural properties of these large and complex networks, they also find practical utility in fields of applied microbiology where they can, for example, be used for the design of genetic manipulation strategies, for defining novel media compositions, identifying maximum yields, or as potential targets for new antibiotic compounds. Although most work in the field is focused on prokaryotes, models have also been published for a variety of single and multicellular eukaryotes; the underlying principles remain the same, although eukaryotic models do pose a number of additional challenges. The objective of this Special Issue is twofold: firstly, to provide an overview and introduction to researchers who are new to the area, and secondly to provide a platform from which new models and algorithms can be presented.

Dr. Mark M.G. Poolman
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 modeling
  • Genome-scale metabolic models
  • Microbiology
  • Biotechnology
  • Flux balance analysis
  • Computational biology

Published Papers (5 papers)

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Research

Jump to: Review

20 pages, 41179 KiB  
Article
Inspecting the Solution Space of Genome-Scale Metabolic Models
by Seyed Babak Loghmani, Nadine Veith, Sven Sahle, Frank T. Bergmann, Brett G. Olivier and Ursula Kummer
Metabolites 2022, 12(1), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12010043 - 05 Jan 2022
Cited by 4 | Viewed by 1819
Abstract
Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models [...] Read more.
Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results. Full article
(This article belongs to the Special Issue Genome-Scale Metabolic Models)
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12 pages, 11368 KiB  
Article
Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models
by Andrea Cabbia, Peter A. J. Hilbers and Natal A. W. van Riel
Metabolites 2021, 11(10), 695; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11100695 - 12 Oct 2021
Viewed by 2024
Abstract
Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility [...] Read more.
Metabolic flexibility is the ability of an organism to adapt its energy source based on nutrient availability and energy requirements. In humans, this ability has been linked to cardio-metabolic health and healthy aging. Genome-scale metabolic models have been employed to simulate metabolic flexibility by computing the Respiratory Quotient (RQ), which is defined as the ratio of carbon dioxide produced to oxygen consumed, and varies between values of 0.7 for pure fat metabolism and 1.0 for pure carbohydrate metabolism. While the nutritional determinants of metabolic flexibility are known, the role of low energy expenditure and sedentary behavior in the development of metabolic inflexibility is less studied. In this study, we present a new description of metabolic flexibility in genome-scale metabolic models which accounts for energy expenditure, and we study the interactions between physical activity and nutrition in a set of patient-derived models of skeletal muscle metabolism in older adults. The simulations show that fuel choice is sensitive to ATP consumption rate in all models tested. The ability to adapt fuel utilization to energy demands is an intrinsic property of the metabolic network. Full article
(This article belongs to the Special Issue Genome-Scale Metabolic Models)
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17 pages, 1205 KiB  
Article
A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers
by John I. Hendry, Hoang V. Dinh, Debolina Sarkar, Lin Wang, Anindita Bandyopadhyay, Himadri B. Pakrasi and Costas D. Maranas
Metabolites 2021, 11(3), 168; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11030168 - 15 Mar 2021
Cited by 4 | Viewed by 2560
Abstract
Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a [...] Read more.
Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a comprehensive genome-scale metabolic (GSM) model, iAnC892, for this organism using annotations and content obtained from multiple databases. iAnC892 describes both the vegetative and heterocyst cell types found in the filaments of Anabaena sp. ATCC 33047. iAnC892 includes 953 unique reactions and accounts for the annotation of 892 genes. Comparison of iAnC892 reaction content with the GSM of Anabaena sp. PCC 7120 revealed that there are 109 reactions including uptake hydrogenase, pyruvate decarboxylase, and pyruvate-formate lyase unique to iAnC892. iAnC892 enabled the analysis of energy production pathways in the heterocyst by allowing the cell specific deactivation of light dependent electron transport chain and glucose-6-phosphate metabolizing pathways. The analysis revealed the importance of light dependent electron transport in generating ATP and NADPH at the required ratio for optimal N2 fixation. When used alongside the strain design algorithm, OptForce, iAnC892 recapitulated several of the experimentally successful genetic intervention strategies that over produced valerolactam and caprolactam precursors. Full article
(This article belongs to the Special Issue Genome-Scale Metabolic Models)
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Review

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27 pages, 3341 KiB  
Review
Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data
by Anurag Passi, Juan D. Tibocha-Bonilla, Manish Kumar, Diego Tec-Campos, Karsten Zengler and Cristal Zuniga
Metabolites 2022, 12(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo12010014 - 24 Dec 2021
Cited by 30 | Viewed by 7120
Abstract
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze [...] Read more.
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data. Full article
(This article belongs to the Special Issue Genome-Scale Metabolic Models)
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20 pages, 17286 KiB  
Review
Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms
by Carolina H. Chung, Da-Wei Lin, Alec Eames and Sriram Chandrasekaran
Metabolites 2021, 11(9), 606; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11090606 - 07 Sep 2021
Cited by 18 | Viewed by 3874
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range [...] Read more.
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms. Full article
(This article belongs to the Special Issue Genome-Scale Metabolic Models)
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