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Metabolites
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Computational Biology for Metabolic Modelling
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Computational Biology for Metabolic Modelling

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 July 2021) | Viewed by 32875

Special Issue Editors

Prof. Dario Pescini

E-Mail Website
Guest Editor
Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milano, Italy
Interests: systems biology; metabolic modelling; mechanicistic modelling
Dr. Marzia Di Filippo

E-Mail Website
Co-Guest Editor
Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milano, Italy
Interests: systems biology; metabolic modelling; constraint-based modelling

Special Issue Information

Dear Colleagues,

Metabolic profiling of a cell provides a comprehensive and functional view of the biochemistry connecting its genome to a particular phenotype following the interplay between cell and its environment. In this regard, in silico metabolic models provide structured frameworks for investigating, at a systemic level, metabolic capabilities of a given cell or organism as complex genotype–phenotype relationships.

Thanks to the development of high-throughput technologies, a massive amount of large-scale datasets is generated. In this context, the definition of the rules underlying each biochemical reaction in terms of relationships among catalyzing genes allows us to further extend the usefulness of metabolic models as scaffolds for the integration of omics data in order to create context-specific models, where the active metabolic network in a given cell or organism is unraveled together with insights of its metabolic capabilities more close to reality.

To compound issues further, cell-to-cell metabolic variability is known to be constantly present in any population of cells, where intricate dialogues between cells and their environment lead to the selection of individuals that are best phenotypically adapted to survive, leading to heterogeneous phenotypes. These interactions characterize not only individual organisms, such as cancer cell populations, but also communities of multiple organisms as occurring in given microbiome communities, and between host and microbiome.

In this Special Issue, we ask for contributions dealing with various facets of metabolic modeling, including reconstruction of metabolic networks, constraint-based and mechanism-based metabolic modeling, omics data integration, machine learning approaches, and community-scale modeling.

Prof. Dario Pescini
Dr. Marzia Di Filippo
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

  • Reconstruction of metabolic networks
  • Kinetic modeling
  • Communities-scale modeling (cell populations, metagenome)
  • Omics data integration
  • Metabolism regulation
  • Machine learning

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Published Papers (10 papers)

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Research

13 pages, 461 KiB  
Article
Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A
by Teresa Díaz Calvo, Noemi Tejera, Iain McNamara, Gemma C. Langridge, John Wain, Mark Poolman and Dipali Singh
Metabolites 2022, 12(2), 136; https://doi.org/10.3390/metabo12020136 - 2 Feb 2022
Cited by 8 | Viewed by 3467
Abstract
Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind [...] Read more.
Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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9 pages, 862 KiB  
Article
MetAMDB: Metabolic Atom Mapping Database
by Collin Starke and Andre Wegner
Metabolites 2022, 12(2), 122; https://doi.org/10.3390/metabo12020122 - 27 Jan 2022
Cited by 3 | Viewed by 2985
Abstract
MetAMDB is an open-source metabolic atom mapping database, providing atom mappings for around 43,000 metabolic reactions. Each atom mapping can be inspected and downloaded either as an RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of [...] Read more.
MetAMDB is an open-source metabolic atom mapping database, providing atom mappings for around 43,000 metabolic reactions. Each atom mapping can be inspected and downloaded either as an RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome-scale) and can subsequently be used in standard 13C metabolic flux analysis software. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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19 pages, 1329 KiB  
Article
Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States
by Wolfram Liebermeister and Elad Noor
Metabolites 2021, 11(11), 749; https://doi.org/10.3390/metabo11110749 - 29 Oct 2021
Cited by 5 | Viewed by 2901
Abstract
Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models [...] Read more.
Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models are large. Here we show how consistent kinetic constants, metabolite concentrations, and enzyme concentrations can be determined from data if metabolic fluxes are known. The estimation method, called model balancing, can handle models with a wide range of rate laws and accounts for thermodynamic constraints between fluxes, kinetic constants, and metabolite concentrations. It can be used to estimate in-vivo kinetic constants, to complete and adjust available data, and to construct plausible metabolic states with predefined flux distributions. By omitting one term from the log posterior—a term for penalising low enzyme concentrations—we obtain a convex optimality problem with a unique local optimum. As a demonstrative case, we balance a model of E. coli central metabolism with artificial or experimental data and obtain a physically and biologically plausible parameterisation of reaction kinetics in E. coli central metabolism. The example shows what information about kinetic constants can be obtained from omics data and reveals practical limits to estimating in-vivo kinetic constants. While noise-free omics data allow for a reasonable reconstruction of in-vivo kcat and KM values, prediction from noisy omics data are worse. Hence, adjusting kinetic constants and omics data to obtain consistent metabolic models is the main application of model balancing. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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21 pages, 2327 KiB  
Article
Perturbation-Based Modeling Unveils the Autophagic Modulation of Chemosensitivity and Immunogenicity in Breast Cancer Cells
by Isaac Quiros-Fernandez, Lucía Figueroa-Protti, Jorge L. Arias-Arias, Norman Brenes-Cordero, Francisco Siles, Javier Mora and Rodrigo Antonio Mora-Rodríguez
Metabolites 2021, 11(9), 637; https://doi.org/10.3390/metabo11090637 - 18 Sep 2021
Cited by 1 | Viewed by 3844
Abstract
In the absence of new therapeutic strategies, chemotherapeutic drugs are the most widely used strategy against metastatic breast cancer, in spite of eliciting multiple adverse effects and having low responses with an average 5-year patient survival rate. Among the new therapeutic targets that [...] Read more.
In the absence of new therapeutic strategies, chemotherapeutic drugs are the most widely used strategy against metastatic breast cancer, in spite of eliciting multiple adverse effects and having low responses with an average 5-year patient survival rate. Among the new therapeutic targets that are currently in clinical trials, here, we addressed the association between the regulation of the metabolic process of autophagy and the exposure of damage-associated molecular patterns associated (DAMPs) to immunogenic cell death (ICD), which has not been previously studied. After validating an mCHR-GFP tandem LC3 sensor capacity to report dynamic changes of the autophagic metabolic flux in response to external stimuli and demonstrating that both basal autophagy levels and response to diverse autophagy regulators fluctuate among different cell lines, we explored the interaction between autophagy modulators and chemotherapeutic agents in regards of cytotoxicity and ICD using three different breast cancer cell lines. Since these interactions are very complex and variable throughout different cell lines, we designed a perturbation-based model in which we propose specific modes of action of chemotherapeutic agents on the autophagic flux and the corresponding strategies of modulation to enhance the response to chemotherapy. Our results point towards a promising therapeutic potential of the metabolic regulation of autophagy to overcome chemotherapy resistance by eliciting ICD. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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13 pages, 1531 KiB  
Article
Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis
by Cheewin Kittikunapong, Suhui Ye, Patricia Magadán-Corpas, Álvaro Pérez-Valero, Claudio J. Villar, Felipe Lombó and Eduard J. Kerkhoven
Metabolites 2021, 11(5), 304; https://doi.org/10.3390/metabo11050304 - 11 May 2021
Cited by 13 | Viewed by 4132
Abstract
Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products [...] Read more.
Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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24 pages, 2366 KiB  
Article
Understanding FBA Solutions under Multiple Nutrient Limitations
by Eunice van Pelt-KleinJan, Daan H. de Groot and Bas Teusink
Metabolites 2021, 11(5), 257; https://doi.org/10.3390/metabo11050257 - 21 Apr 2021
Cited by 4 | Viewed by 3356
Abstract
Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on [...] Read more.
Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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12 pages, 1364 KiB  
Article
Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling
by Ozlem Altay, Cheng Zhang, Hasan Turkez, Jens Nielsen, Mathias Uhlén and Adil Mardinoglu
Metabolites 2021, 11(4), 221; https://doi.org/10.3390/metabo11040221 - 5 Apr 2021
Cited by 7 | Viewed by 3044
Abstract
Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that [...] Read more.
Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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13 pages, 1201 KiB  
Article
The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases
by Julia Koblitz, Sabine Eva Will, S. Alexander Riemer, Thomas Ulas, Meina Neumann-Schaal and Dietmar Schomburg
Metabolites 2021, 11(2), 113; https://doi.org/10.3390/metabo11020113 - 17 Feb 2021
Cited by 2 | Viewed by 3207
Abstract
Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations [...] Read more.
Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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14 pages, 2578 KiB  
Article
In Vivo Metabolism of [1,6-13C2]Glucose Reveals Distinct Neuroenergetic Functionality between Mouse Hippocampus and Hypothalamus
by Antoine Cherix, Rajesh Sonti, Bernard Lanz and Hongxia Lei
Metabolites 2021, 11(1), 50; https://doi.org/10.3390/metabo11010050 - 12 Jan 2021
Cited by 3 | Viewed by 2580
Abstract
Glucose is a major energy fuel for the brain, however, less is known about specificities of its metabolism in distinct cerebral areas. Here we examined the regional differences in glucose utilization between the hypothalamus and hippocampus using in vivo indirect 13C magnetic [...] Read more.
Glucose is a major energy fuel for the brain, however, less is known about specificities of its metabolism in distinct cerebral areas. Here we examined the regional differences in glucose utilization between the hypothalamus and hippocampus using in vivo indirect 13C magnetic resonance spectroscopy (1H-[13C]-MRS) upon infusion of [1,6-13C2]glucose. Using a metabolic flux analysis with a 1-compartment mathematical model of brain metabolism, we report that compared to hippocampus, hypothalamus shows higher levels of aerobic glycolysis associated with a marked gamma-aminobutyric acid-ergic (GABAergic) and astrocytic metabolic dependence. In addition, our analysis suggests a higher rate of ATP production in hypothalamus that is accompanied by an excess of cytosolic nicotinamide adenine dinucleotide (NADH) production that does not fuel mitochondria via the malate-aspartate shuttle (MAS). In conclusion, our results reveal significant metabolic differences, which might be attributable to respective cell populations or functional features of both structures. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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12 pages, 276 KiB  
Article
Flux Coupling and the Objective Functions’ Length in EFMs
by Francisco Guil, José F. Hidalgo and José M. García
Metabolites 2020, 10(12), 489; https://doi.org/10.3390/metabo10120489 - 28 Nov 2020
Cited by 2 | Viewed by 1804
Abstract
Structural analysis of constraint-based metabolic network models attempts to find the network’s properties by searching for subsets of suitable modes or Elementary Flux Modes (EFMs). One useful approach is based on Linear Program (LP) techniques, which introduce an objective function to convert the [...] Read more.
Structural analysis of constraint-based metabolic network models attempts to find the network’s properties by searching for subsets of suitable modes or Elementary Flux Modes (EFMs). One useful approach is based on Linear Program (LP) techniques, which introduce an objective function to convert the stoichiometric and thermodynamic constraints into a linear program (LP), using additional constraints to generate different nontrivial modes. This work introduces FLFS-FC (Fixed Length Function Sampling with Flux Coupling), a new approach to increase the efficiency of generation of large sets of different EFMs for the network. FLFS-FC is based on the importance of the length of the objective functions used in the associated LP problem and the imposition of additional negative constraints. Our proposal overrides some of the known drawbacks associated with the EFM extraction, such as the appearance of unfeasible problems or multiple repeated solutions arising from different LP problems. Full article
(This article belongs to the Special Issue Computational Biology for Metabolic Modelling)
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