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Metabolites
Special Issues
Recent Developments and Emerging Trends in Metabolic Modelling and Metabolomics
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Recent Developments and Emerging Trends in Metabolic Modelling and Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 3687

Special Issue Editors

Prof. Dr. Johnjoe McFadden

E-Mail Website
Guest Editor
Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
Interests: microbial molecular genetics; systems biology; quantum biology; tuberculosis
Dr. Khushboo Borah Slater

E-Mail Website
Guest Editor
Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
Interests: microbial molecular genetics; systems biology; quantum biology; tuberculosis

Special Issue Information

Dear Colleagues,

Metabolism is critical to the growth and functioning of any biological system. The knowledge of metabolic phenotypes of living cells provides avenues for the development of new therapies and the identification of biomarkers for various diseases, or in metabolic engineering for industrial applications. Metabolomics and systems-based tools such as metabolic flux analysis and genome-scale modelling are popular approaches for measuring metabolism. There have been continuous efforts in refining and advancing analytical platforms and flux analysis tools to provide detailed insights into cellular metabolism and metabolic fluxes. This Special Issue focuses on recent developments and emerging trends in metabolomic studies and in metabolic modelling. It will cover the application of these tools in various disciplines ranging from human diseases to microbial biotechnology.

Prof. Dr. Johnjoe McFadden
Dr. Khushboo Borah Slater
Guest Editors

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

  • constraint-based modelling
  • genome scale models
  • metabolic flux analysis
  • metabolomics
  • infectious disease

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Published Papers (2 papers)

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20 pages, 7736 KiB  
Article
Predicting the Rate Structure of an Evolved Metabolic Network
by Friedrich Srienc and John Barrett
Metabolites 2025, 15(3), 200; https://doi.org/10.3390/metabo15030200 - 13 Mar 2025
Viewed by 463
Abstract
Background: When glucose molecules are metabolized by a biological cell, the molecules are constrained to flow along distinct reaction trajectories, which are defined by the cell’s underlying metabolic network. Methods: Using the computational technique of Elementary Mode Analysis, the entire set [...] Read more.
Background: When glucose molecules are metabolized by a biological cell, the molecules are constrained to flow along distinct reaction trajectories, which are defined by the cell’s underlying metabolic network. Methods: Using the computational technique of Elementary Mode Analysis, the entire set of all possible trajectories can be enumerated, effectively allowing metabolism to be viewed in a discretized space. Results: With the resulting set of Elementary Flux Modes (EMs), macroscopic fluxes, (of both mass and energy) that cross the cell envelope can be computed by a simple, linear combination of the individual EM trajectories. The challenge in this approach is that the usage probability of each EM is unknown. But, because the analytical framework we have adopted allows metabolism to be viewed in a discrete space, we can use the mathematics of statistical thermodynamics to derive the usage probabilities when the system entropy is maximized. The resulting probabilities, which obey a Boltzmann-type distribution, predict a rate structure for the metabolic network that is in remarkable agreement with experimentally measured rates of adaptively evolved E. coli strains. Conclusions: Thus, in principle, the intracellular dynamic properties of such bacteria can be predicted, using only the knowledge of the DNA sequence, to reconstruct the metabolic reaction network, and the measurement of the specific glucose uptake rate. Full article
(This article belongs to the Special Issue Recent Developments and Emerging Trends in Metabolic Modelling and Metabolomics)
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Review

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23 pages, 1784 KiB  
Review
Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective
by Wheaton L. Schroeder, Patrick F. Suthers, Thomas C. Willis, Eric J. Mooney and Costas D. Maranas
Metabolites 2024, 14(7), 365; https://doi.org/10.3390/metabo14070365 - 28 Jun 2024
Cited by 3 | Viewed by 2299
Abstract
Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and [...] Read more.
Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed. Full article
(This article belongs to the Special Issue Recent Developments and Emerging Trends in Metabolic Modelling and Metabolomics)
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