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 December 2024 | Viewed by 2113

Special Issue Editors


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

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

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Keywords

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

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Published Papers (1 paper)

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Review

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
Viewed by 1474
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
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