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Metabolites
Special Issues
Metabolic Modelling: Methods, Applications and Future Perspectives
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Metabolic Modelling: Methods, Applications and Future Perspectives

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7478

Special Issue Editors

Dr. Marco Fondi

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Guest Editor
Department of Biology, University of Florence, 50019 Florence, Italy
Interests: systems biology; evolutionary genomics; metabolic modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Claudio Angione

E-Mail Website
Guest Editor
Department of Computer Science and Information Systems, Teesside University, Campus Heart, Middlesbrough TS1 3BX, UK
Interests: metabolic modelling; multi-omic integration; machine learning
Dr. Rodrigo Ledesma-Amaro

E-Mail Website
Guest Editor
Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK
Interests: synthetic biology; metabolic engineering; bioproduction; microbial communities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolic modelling refers to a wide range of computational techniques that allow investigating, understanding and predicting cellular metabolism at the system level. Metabolic modelling is central in many disciplines, from synthetic to environmental biology, from theoretical to systems biology. Predictions obtained with such an approach can be used to guide focused experimental design, thus, reducing the search space during wet-lab practices. Additionally, some models (e.g., genome-scale metabolic reconstructions) are excellent scaffolds for the integration of -omics data, providing a context-specific, or patient-specific understanding of major biological circuits. A plethora of methods and approaches to computing and predicting metabolic phenotype exists, ranging from deterministic static approaches to modelling frameworks that account for molecular kinetics and cellular stochasticity in general. This Special Issue is devoted to reviewing the current practical and theoretical aspects of metabolic modelling workflows, starting from the basic theory behind such modelling frameworks to the implementation of modelling outcomes. We, therefore, invite research articles, review and viewpoint manuscripts devoted to various aspects within metabolic modelling, with a specific emphasis on omics data integration, dynamic modelling, constraint-based metabolic modelling, ODE-based modelling and metabolic engineering, as well as those highlighting the best practices for experimental-modelling integration

Dr. Marco Fondi
Dr. Claudio Angione
Dr. Rodrigo Ledesma-Amaro
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

  • Metabolic modelling
  • kinetic modelling
  • constraint-based metabolic modelling
  • genome-scale metabolic reconstruction
  • machine learning
  • ODEs
  • community modelling
  • flux Balance Analysis

Published Papers (2 papers)

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15 pages, 1645 KiB  
Article
Modelling hCDKL5 Heterologous Expression in Bacteria
by Marco Fondi, Stefano Gonzi, Mikolaj Dziurzynski, Paola Turano, Veronica Ghini, Marzia Calvanese, Andrea Colarusso, Concetta Lauro, Ermenegilda Parrilli and Maria Luisa Tutino
Metabolites 2021, 11(8), 491; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11080491 - 28 Jul 2021
Cited by 4 | Viewed by 2185
Abstract
hCDKL5 refers to the human cyclin-dependent kinase like 5 that is primarily expressed in the brain. Mutations in its coding sequence are often causative of hCDKL5 deficiency disorder, a devastating neurodevelopmental disorder currently lacking a cure. The large-scale recombinant production of hCDKL5 is [...] Read more.
hCDKL5 refers to the human cyclin-dependent kinase like 5 that is primarily expressed in the brain. Mutations in its coding sequence are often causative of hCDKL5 deficiency disorder, a devastating neurodevelopmental disorder currently lacking a cure. The large-scale recombinant production of hCDKL5 is desirable to boost the translation of preclinical therapeutic approaches into the clinic. However, this is hampered by the intrinsically disordered nature of almost two-thirds of the hCDKL5 sequence, making this region more susceptible to proteolytic attack, and the observed toxicity when the enzyme is accumulated in the cytoplasm of eukaryotic host cells. The bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) is the only prokaryotic host in which the full-length production of hCDKL5 has been demonstrated. To date, a system-level understanding of the metabolic burden imposed by hCDKL5 production is missing, although it would be crucial for upscaling of the production process. Here, we combined experimental data on protein production and nutrients assimilation with metabolic modelling to infer the global consequences of hCDKL5 production in PhTAC125 and to identify potential overproduction targets. Our analyses showed a remarkable accuracy of the model in simulating the recombinant strain phenotype and also identified priority targets for optimised protein production. Full article
(This article belongs to the Special Issue Metabolic Modelling: Methods, Applications and Future Perspectives)
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14 pages, 1599 KiB  
Article
Identifying Personalized Metabolic Signatures in Breast Cancer
by Priyanka Baloni, Wikum Dinalankara, John C. Earls, Theo A. Knijnenburg, Donald Geman, Luigi Marchionni and Nathan D. Price
Metabolites 2021, 11(1), 20; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11010020 - 30 Dec 2020
Cited by 9 | Viewed by 4182
Abstract
Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on [...] Read more.
Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach. Full article
(This article belongs to the Special Issue Metabolic Modelling: Methods, Applications and Future Perspectives)
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