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Metabolites
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Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques
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Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 6297

Special Issue Editor

Dr. Priyanka Baloni

E-Mail Website
Guest Editor
School of Health Sciences, Purdue University, West Lafayette, IN 47906, USA
Interests: metabolic modeling; neurodegenerative diseases; toxicology; multi-omics analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques”, seeks to explore the cutting-edge developments and applications of genome-scale metabolic models (GEMs) and related techniques in understanding complex biological systems. With the rapid advancement of high-throughput omics technologies, GEMs have emerged as powerful tools for mapping and simulating the intricate networks of metabolic pathways across various organisms. This Special Issue invites researchers to submit original research, reviews, and case studies that highlight innovative approaches to genome-scale modeling, including the integration of multi-omics data, machine learning techniques, and novel algorithms. This Special Issue will focus on the integration of the computational biology, systems biology, and bioinformatics fields along with experimental data to analyze and predict the behavior of complex metabolic systems at the cellular level.

Studies exploring innovative methodologies in genome-scale modeling, including constraint-based models, flux balance analysis, and integration with multi-omics data offering novel insights into applications in health, disease, and biotechnology are highly welcomed. This Special Issue aims to showcase how these techniques can be used to understand metabolic regulation, identify potential drug targets, and optimize metabolic engineering efforts. Researchers are invited to contribute original research, reviews, and case studies that advance the understanding of metabolic pathways through the lens of genome-scale modeling.

Dr. Priyanka Baloni
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 250 words) can be sent to the Editorial Office for assessment.

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

  • metabolism
  • systems biology
  • genome-scale metabolic modeling
  • multi-omics data
  • data integration
  • data analysis
  • metabolic regulation
  • biomarker
  • drug targets

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Published Papers (4 papers)

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Research

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26 pages, 1671 KB  
Article
Evaluation of Genome-Scale Model Reconstruction Strategies for Lentilactobacillus kefiri DH5 and Deciphering Its Metabolic Network
by Maryam. A. Esembaeva, Mikhail A. Kulyashov, Tatiana S. Sokolova, Ilya R. Akberdin and Alexey E. Sazonov
Metabolites 2025, 15(12), 767; https://doi.org/10.3390/metabo15120767 - 26 Nov 2025
Viewed by 392
Abstract
Background/Objectives: Genome-scale metabolic models (GSM) are key tools for predicting microbial physiology, yet species within the genus Lentilactobacillus remain largely unexplored. Lentilactobacillus kefiri DH5 is an obligately heterofermentative lactic acid bacterium with unique redox metabolism, but no curated GSM model exists for this [...] Read more.
Background/Objectives: Genome-scale metabolic models (GSM) are key tools for predicting microbial physiology, yet species within the genus Lentilactobacillus remain largely unexplored. Lentilactobacillus kefiri DH5 is an obligately heterofermentative lactic acid bacterium with unique redox metabolism, but no curated GSM model exists for this species. This study aimed to generate the first GSM model for L. kefiri DH5, evaluate multiple reconstruction tools, and characterize metabolic features underlying its heterofermentative metabolism. Methods: Draft GSM models were generated from the L. kefiri DH5 genome annotation using five reconstruction tools. For each tool, gap-filling was performed on a CDM, followed by quality assessment using the MEMOTE. Manual curation was performed using the COBRApy library. Results: Among the five reconstructions, the KBase-derived draft demonstrated the highest quality and production potential for metabolites characteristic of heterofermentative fermentation. During manual curation of this model, reaction directions in central carbon metabolism and amino acid pathways were corrected. Analysis further identified an alternative NADH-regenerating glucose shunt via D-gluconate, supported by omics data and enzyme promiscuity considerations. Incorporation of this pathway resolved the redox imbalance and allowed the model to reproduce metabolic exchange profiles characteristic of obligate heterofermenters. Conclusions: We developed the first manually curated genome-scale model of L. kefiri DH5 and showed that the choice of reconstruction tool substantially affects model quality and predictive power. We also proposed an alternative glucose assimilation shunt via gluconolactone, which resolved the redox imbalance in the model and enabled representation of the heterofermentative metabolism. Full article
(This article belongs to the Special Issue Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques)
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23 pages, 6611 KB  
Article
Investigating Lipid and Energy Dyshomeostasis Induced by Per- and Polyfluoroalkyl Substances (PFAS) Congeners in Mouse Model Using Systems Biology Approaches
by Esraa Gabal, Marwah Azaizeh and Priyanka Baloni
Metabolites 2025, 15(8), 499; https://doi.org/10.3390/metabo15080499 - 24 Jul 2025
Cited by 1 | Viewed by 1681
Abstract
Background: Exposure to per- and polyfluoroalkyl substances (PFAS, including 7H-Perfluoro-4-methyl-3,6-dioxaoctanesulfonic acid (PFESA-BP2), perfluorooctanoic acid (PFOA), and hexafluoropropylene oxide (GenX), has been associated with liver dysfunction. While previous research has characterized PFAS-induced hepatic lipid alterations, their downstream effects on energy metabolism remain unclear. This [...] Read more.
Background: Exposure to per- and polyfluoroalkyl substances (PFAS, including 7H-Perfluoro-4-methyl-3,6-dioxaoctanesulfonic acid (PFESA-BP2), perfluorooctanoic acid (PFOA), and hexafluoropropylene oxide (GenX), has been associated with liver dysfunction. While previous research has characterized PFAS-induced hepatic lipid alterations, their downstream effects on energy metabolism remain unclear. This study investigates metabolic alterations in the liver following PFAS exposure to identify mechanisms leading to hepatoxicity. Methods: We analyzed RNA sequencing datasets of mouse liver tissues exposed to PFAS to identify metabolic pathways influenced by the chemical toxicant. We integrated the transcriptome data with a mouse genome-scale metabolic model to perform in silico flux analysis and investigated reactions and genes associated with lipid and energy metabolism. Results: PFESA-BP2 exposure caused dose- and sex-dependent changes, including upregulation of fatty acid metabolism, β-oxidation, and cholesterol biosynthesis. On the contrary, triglycerides, sphingolipids, and glycerophospholipids metabolism were suppressed. Simulations from the integrated genome-scale metabolic models confirmed increased flux for mevalonate and lanosterol metabolism, supporting potential cholesterol accumulation. GenX and PFOA triggered strong PPARα-dependent responses, especially in β-oxidation and lipolysis, which were attenuated in PPARα−/− mice. Mitochondrial fatty acid transport and acylcarnitine turnover were also disrupted, suggesting impaired mitochondrial dysfunction. Additional PFAS effects included perturbations in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and blood–brain barrier (BBB) function, pointing to broader systemic toxicity. Conclusions: Our findings highlight key metabolic signatures and suggest PFAS-mediated disruption of hepatic and possibly neurological functions. This study underscores the utility of genome-scale metabolic modeling as a powerful tool to interpret transcriptomic data and predict systemic metabolic outcomes of toxicant exposure. Full article
(This article belongs to the Special Issue Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques)
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12 pages, 395 KB  
Article
Adapting OptCouple to Identify Strategies with Increased Product Yields in Community Cohorts of E. coli
by Nicole Pearcy and Jamie Twycross
Metabolites 2025, 15(5), 309; https://doi.org/10.3390/metabo15050309 - 6 May 2025
Viewed by 684
Abstract
Background: Microbesas chemical factories provide an alternative sustainable approach for producing platform chemicals. Until recently, most efforts have involved engineering heterologous pathways into a single microbial chassis to maximise its production of a target chemical. More recently, cohorts of microbes have been used [...] Read more.
Background: Microbesas chemical factories provide an alternative sustainable approach for producing platform chemicals. Until recently, most efforts have involved engineering heterologous pathways into a single microbial chassis to maximise its production of a target chemical. More recently, cohorts of microbes have been used to engineer microbial communities to achieve higher yields than achieved in a single chassis. Full article
(This article belongs to the Special Issue Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques)
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Review

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25 pages, 2912 KB  
Review
Metabolic Objectives and Trade-Offs: Inference and Applications
by Da-Wei Lin, Saanjh Khattar and Sriram Chandrasekaran
Metabolites 2025, 15(2), 101; https://doi.org/10.3390/metabo15020101 - 6 Feb 2025
Cited by 1 | Viewed by 2782
Abstract
Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and [...] Read more.
Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology. Full article
(This article belongs to the Special Issue Comprehensive Insights into Metabolic Pathways: Genome-Scale Modeling Techniques)
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