Growth Dependent Computation of Chokepoints in Metabolic Networks

Oarga, Alexandru; Bannerman, Bridget P.; Júlvez, Jorge

doi:10.1007/978-3-030-60327-4_6

Cited by 4 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bottleneck reactions identified by the findCPcli tool are unique reactions of the metabolic network required for the growth and survival of the organism and, like chokepoint reactions, are potential drug targets ( Yeh et al, 2004 ; Oarga et al, 2020 ). Although classical chokepoint reactions identify reactions that are the only producers or the only consumers of a given metabolite and consider just the model structure, we improve on this approach by using both the structural and dynamical information.…”

Section: Resultsmentioning

confidence: 99%

“…We also provide additional resources to the COVID-19 disease map ( Ostaszewski et al, 2020 ). In addition, we have designed a new algorithm ( Oarga et al, 2020 ) and used the dynamic information of the human/virus models to predict new treatment regimens. Here, we demonstrate the flux changes from lipid metabolism, cofactor biosynthetic pathways, redox balance, and immune regulation indicative of pathogenic reactions arising from the COVID-19 viral infections.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

et al. 2021

Self Cite

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus’ pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https://www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool, findCPcli, that implements the computational method is available at https://github.com/findCP/findCPcli.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to capture the fact that reactions can proceed forwards or backwards, e.g. reversible reactions, new sets of reactants, products, consumers and producers that take into account flux bounds were defined ( Oarga et al , 2020 ):…”

Section: Systems and Methodsmentioning

confidence: 99%

CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities

2023

View full text Add to dashboard Cite

Motivation Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets. Results This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities. Availability CONTRABASS is available as an open source Python package at https://github.com/openCONTRABASS/CONTRABASS under GPL-3.0 License. An online web server is available at http://contrabass.unizar.es. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

“…An illustration of the distribution of unique enzymes and bottleneck reactions in each of the models (iMab22 and iMlep22) in comparison with each other and with M. tuberculosis is demonstrated in Figure 3. Alternative enzymes are not included in this analysis because they catalyse the same biochemical reactions and do not fit into the category of bottleneck reactions, which are defined as unique reactions responsible for the survival and growth of the organisms in the metabolic network [Bannerman et al, 2021;Yeh et al, 2004;Oarga et al, 2020].…”

Section: Comparative Analysismentioning

confidence: 99%

“…Here we demonstrate the newly built genome-scale metabolic models of M. leprae and M. abscessus, including the curation, simulation, and model optimization strategies [Ebrahim et al, 2013, Oarga et al, 2020, Bannerman et al, 2021. To ensure the development of standardised metabolic models for the global systems biology community, we have implemented the recently released community standards and used the MEMOTE quality control software to evaluate our models [Carey et al, 2020, Lieven et al, 2020.…”

mentioning

confidence: 99%

Mycobacterial Metabolic Model Development for Drug Target Identification

Bannerman

Oarga

Júlvez

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are the source of widespread infections in humans. According to the World Health Organization, Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second largest infectious disease killer worldwide after COVID-19; Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other health-care-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical to develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseased conditions. In this study, metabolic models have been developed for two bacterial pathogens, M. leprae, and M. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets of each pathogen are significant for precision medicine initiatives. The models and associating datasets are available in GigaScience and the following repositories: M. abscessus Biomodels: https://www.ebi.ac.uk/biomodels/MODEL2203300002; https://www.patmedb.org/Bacteria/Mabscessus M. leprae Biomodels: https://www.ebi.ac.uk/biomodels/MODEL2203300001; https://www.patmedb.org/Bacteria/Mleprae

show abstract

Growth Dependent Computation of Chokepoints in Metabolic Networks

Cited by 4 publications

References 20 publications

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities

Mycobacterial Metabolic Model Development for Drug Target Identification

Contact Info

Product

Resources

About