scholarly journals Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Metabolites ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Anurag Passi ◽  
Juan D. Tibocha-Bonilla ◽  
Manish Kumar ◽  
Diego Tec-Campos ◽  
Karsten Zengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Metabolic Pathways ◽  
Drug Targets ◽  
Metabolic Modeling ◽  
Annotation Data ◽  
Dynamic Resolution ◽  
Different Types ◽  
Eukaryotic Organisms ◽  
Genome Scale

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.

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

scholarly journals The era of big data: Genome-scale modelling meets machine learning

2020 ◽  
Vol 18 ◽  
pp. 3287-3300 ◽  
Author(s):  
Athanasios Antonakoudis ◽  
Rodrigo Barbosa ◽  
Pavlos Kotidis ◽  
Cleo Kontoravdi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Scale Modelling ◽  
Genome Scale

scholarly journals In silicoidentification of metabolic enzyme drug targets inBurkholderia pseudomallei

2015 ◽  
Author(s):  
Jean F. Challacombe
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Culture Media ◽  
Metabolic Modeling ◽  
Host Cells ◽  
Natural Resistance ◽  
Metabolic Enzyme ◽  
Chronic Infections ◽  
Genome Scale ◽  
Nutrient Conditions

AbstractThe intracellular pathogenBurkholderia pseudomallei,which is endemic to parts of southeast Asia and northern Australia, causes the disease melioidosis. Although acute infections can be treated with antibiotics, melioidosis is difficult to cure, and some patients develop chronic infections or a recrudescence of the disease months or years after treatment of the initial infection.B. pseudomalleistrains have a high level of natural resistance to a variety of antibiotics, and with limited options for new antibiotics on the horizon, new alternatives are needed. The aim of the present study was to characterize the metabolic capabilities ofB. pseudomallei, identify metabolites crucial for pathogen survival, understand the metabolic interactions that occur between pathogen and host cells, and determine if metabolic enzymes produced by the pathogen might be potential antibacterial targets. This aim was accomplished through genome scale metabolic modeling under different external conditions: 1) including all nutrients that could be consumed by the model, and 2) providing only the nutrients available in culture media. Using this approach, candidate chokepoint enzymes were identified, then knocked outin silicounder the different nutrient conditions. The effect of each knockout on the metabolic network was examined. When five of the candidate chokepoints were knocked outin silico, the flux through theB. pseudomalleinetwork was decreased, depending on the nutrient conditions. These results demonstrate the utility of genome-scale metabolic modeling methods for drug target identification inB. pseudomallei.

scholarly journals Cellular Genome-scale Metabolic Modeling Identifies New Potential Drug Targets Against Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Oveis Jamialahmadi ◽  
Ehsan Salehabadi ◽  
Sameereh Hashemi-Najafabadi ◽  
Ehsan Motamedian ◽  
Fatemeh Bagheri ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepg2 Cells ◽  
Drug Targets ◽  
Cancer Metabolism ◽  
Essential Gene ◽  
Metabolic Modeling ◽  
Essential Genes ◽  
Potential Drug ◽  
Genome Scale ◽  
Potential Drug Targets

Abstract Hepatocellular carcinoma is the third leading cause of cancer related mortality worldwide. Often this hepatic cancer is associated with fatty liver disease and insulin resistance with genetic predisposition are its major driver. Genome-scale metabolic modeling (GEM) is a promising approach to understand cancer metabolism and to identify new drug targets. Here, we used TRFBA-CORE, an algorithm generating a model using key growth-correlated reactions. Specifically, we generated a HepG2 cell-specific GEM by integrating this cell line transcriptomic data with a generic human metabolic model to predict potential drug targets for hepatocellular carcinoma (HCC). A total of 108 essential genes for growth were predicted by TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterols and steroids biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of 9 drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Interestingly, some of these drugs in this model performed better than Sorafenib, the first line therapeutic against HCC.

scholarly journals Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
David M Curran ◽  
Alexandra Grote ◽  
Nirvana Nursimulu ◽  
Adam Geber ◽  
Dennis Voronin ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Endosymbiotic Bacteria ◽  
Parasitic Worm ◽  
Eukaryotic Organisms ◽  
New Treatments ◽  
Genome Scale ◽  
Novel Drug ◽  
Novel Drug Targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia—present in many filariae—which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2020 ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Novel metabolic biomarkers differentiating radiation-sensitive and -resistant tumors were predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers showed improved classification accuracy, identified novel clinical patient subgroups, and demonstrated the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

scholarly journals Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Keren Yizhak ◽  
Edoardo Gaude ◽  
Sylvia Le Dévédec ◽  
Yedael Y Waldman ◽  
Gideon Y Stein ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Modeling ◽  
Molecular Data ◽  
Metabolic Effects ◽  
Proof Of Concept ◽  
Phenotypic Data ◽  
Experimental Proof ◽  
Physiological Models ◽  
Genome Scale ◽  
Computational Basis

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.

scholarly journals Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

2019 ◽  
Author(s):  
DM Curran ◽  
A Grote ◽  
N Nursimulu ◽  
A Geber ◽  
D Voronin ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Metabolic Model ◽  
Endosymbiotic Bacteria ◽  
Parasitic Worm ◽  
Eukaryotic Organisms ◽  
New Treatments ◽  
Genome Scale ◽  
Novel Drug ◽  
Novel Drug Targets

AbstractThe filarial nematodeBrugia malayirepresents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteriaWolbachia—present in many filariae—which is vital to the worm.Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but only recently have been applied to eukaryotic organisms. Here, we presentiDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 99 reactions essential to the survival ofB. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

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