“…Other review papers, e.g. by Eraslan et al [15] , Zampieri et al [10] , Antonakoudis et al [340] and Gilpin et al [341] , raise the issue of the competitive vs. collaborative nature of data-driven and model-based approaches, stressing the importance of using appropriately constrained mathematical models to help AI tools produce new knowledge of biological mechanisms. Klaus Schwab - Founder and CEO of the World Economic Forum We must address, individually and collectively, moral and ethical issues raised by cutting-edge research in artificial intelligence and biotechnology, which will enable significant life extension, designer babies, and memory extraction.…”
Section: Ai Applications In Functional Genomicsmentioning
“…Other review papers, e.g. by Eraslan et al [15] , Zampieri et al [10] , Antonakoudis et al [340] and Gilpin et al [341] , raise the issue of the competitive vs. collaborative nature of data-driven and model-based approaches, stressing the importance of using appropriately constrained mathematical models to help AI tools produce new knowledge of biological mechanisms. Klaus Schwab - Founder and CEO of the World Economic Forum We must address, individually and collectively, moral and ethical issues raised by cutting-edge research in artificial intelligence and biotechnology, which will enable significant life extension, designer babies, and memory extraction.…”
Section: Ai Applications In Functional Genomicsmentioning
“…131 Up to now, machine learning is employed in the reconstruction of draft models, data preprocessing, dimension reduction, omics analysis, and condensing data. 132 So far, the number of studies integrating machine and deep learning methods with genome-scale modeling has been limited, and a shortlist is given by Zampieri et al 131 Nevertheless, the interest in integrating learning algorithms into GEMs have been growing, especially in the past two years. The fact that the advances in our biological knowledge cannot maintain the same pace with the accumulated omics data indicates that novel integrated approaches should be developed in order to deduct useful information from GEMs and omics data.…”
A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe−microbe and microbe−host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.
“…These networks can represent transcriptional regulatory networks [7], protein-protein interactions networks [8], disease networks [9], metabolic networks [10], or even host-microbe networks [11]. Genome-Scale Metabolic Models (GEMs) are a network-based tool that collect all known metabolic information of a biological system, including the genes, enzymes, reactions, associated gene-protein-reaction (GPR) rules, and metabolites [12]. These metabolic networks provide quantitative predictions related to growth or cellular fitness based on GPR relationships.…”
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.
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