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Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models

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

Deadline for manuscript submissions: closed (5 November 2023) | Viewed by 11753

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Special Issue Editors

Dr. Miha Moškon

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Guest Editor

Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: computational biology; modelling and simulations; in-silico analysis; circadian rhythms; genome-scale metabolic modelling

Dr. Tadeja Režen

E-Mail Website
Guest Editor

Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: systems biology; transcriptome; liver diseases circadian rhythm; omics technologies

Special Issue Information

Dear Colleagues,

Genome-scale metabolic models (GEMs) have been vastly employed in different fields of science, ranging from bioengineering and synthetic biology to systems biology and medicine. A relatively wide scope of GEM applications includes the computational analysis of omics data, which complements existing bioinformatics pipelines. In this context, GEMs can not only be used to analyze experimental data, but also to generate and test novel hypotheses using in silico experimentation mainly derived from constraint-based approaches. However, the biological significance of the results of so-called context-specific models (i.e., models adapted to specific data describing a given context) depends on different factors. These include (1) the quality of the reference GEM used as a scaffold for the reconstruction, (2) the selection of a model extraction method (MEM) used in the process of the reconstruction, and (3) the configuration of a MEM and environmental constraints used for the reconstruction. Several challenges that would increase the reproducibility of obtained GEMs as well as the quality of the obtained results should be addressed. Important aims of the constraint-based modelling community also include the proposal of computational pipelines and protocols that would allow for a straightforward reconstruction and analysis of high-quality context-specific GEMs. Additionally, the experimental validation of hypotheses generated in silico using GEMs is needed. This Special Issue is devoted to original scientific papers as well as reviews describing recent efforts in the context of the reconstruction of context-specific GEMs, their validation, and analysis.

Dr. Miha Moškon
Dr. Tadeja Režen
Guest Editors

Manuscript Submission Information

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Keywords

context-specific genome scale metabolic modelling
constraint-based modelling
omics data integration
model extraction method
computational pipeline
metabolic fluxes
context-specific model

Published Papers (5 papers)

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Research

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15 pages, 2682 KiB

Open AccessArticle

Temperature Dependence of Platelet Metabolism

by Freyr Jóhannsson, James T. Yurkovich, Steinn Guðmundsson, Ólafur E. Sigurjónsson and Óttar Rolfsson

Metabolites 2024, 14(2), 91; https://doi.org/10.3390/metabo14020091 - 26 Jan 2024

Cited by 1 | Viewed by 1400

Abstract

Temperature plays a fundamental role in biology, influencing cellular function, chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is poorly understood, and it remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we used time-course metabolomic data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in a platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type relationship. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperatures indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways. Full article

(This article belongs to the Special Issue Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models)

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16 pages, 2533 KiB

Open AccessArticle

Differential Expression Analysis Utilizing Condition-Specific Metabolic Pathways

by Gianluca Mattei, Zhuohui Gan, Matteo Ramazzotti, Bernhard O. Palsson and Daniel C. Zielinski

Metabolites 2023, 13(11), 1127; https://doi.org/10.3390/metabo13111127 - 3 Nov 2023

Viewed by 1129

Abstract

Pathway analysis is ubiquitous in biological data analysis due to the ability to integrate small simultaneous changes in functionally related components. While pathways are often defined based on either manual curation or network topological properties, an attractive alternative is to generate pathways around specific functions, in which metabolism can be defined as the production and consumption of specific metabolites. In this work, we present an algorithm, termed MetPath, that calculates pathways for condition-specific production and consumption of specific metabolites. We demonstrate that these pathways have several useful properties. Pathways calculated in this manner (1) take into account the condition-specific metabolic role of a gene product, (2) are localized around defined metabolic functions, and (3) quantitatively weigh the importance of expression to a function based on the flux contribution of the gene product. We demonstrate how these pathways elucidate network interactions between genes across different growth conditions and between cell types. Furthermore, the calculated pathways compare favorably to manually curated pathways in predicting the expression correlation between genes. To facilitate the use of these pathways, we have generated a large compendium of pathways under different growth conditions for E. coli. The MetPath algorithm provides a useful tool for metabolic network-based statistical analyses of high-throughput data. Full article

(This article belongs to the Special Issue Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models)

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Graphical abstract

22 pages, 6366 KiB

Open AccessArticle

Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach

by Akansha Srivastava and Palakkad Krishnanunni Vinod

Metabolites 2023, 13(3), 409; https://doi.org/10.3390/metabo13030409 - 9 Mar 2023

Cited by 1 | Viewed by 1959

Abstract

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC. Full article

(This article belongs to the Special Issue Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models)

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Review

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19 pages, 1958 KiB

Open AccessReview

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

by Partho Sen and Matej Orešič

Metabolites 2023, 13(7), 855; https://doi.org/10.3390/metabo13070855 - 18 Jul 2023

Cited by 1 | Viewed by 3717

Abstract

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease. Full article

(This article belongs to the Special Issue Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models)

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28 pages, 590 KiB

Open AccessReview

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

by Miha Moškon and Tadeja Režen

Metabolites 2023, 13(1), 126; https://doi.org/10.3390/metabo13010126 - 13 Jan 2023

Cited by 6 | Viewed by 2496

Abstract

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets. Full article

(This article belongs to the Special Issue Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models)

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