Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart
Abstract In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the eliminatio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05487-7 |
| _version_ | 1797556111857942528 |
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| author | Sz-Wei Chu Feng-Sheng Wang |
| author_facet | Sz-Wei Chu Feng-Sheng Wang |
| author_sort | Sz-Wei Chu |
| collection | DOAJ |
| description | Abstract In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco’s modified eagle medium (DMEM) and Ham’s medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham’s medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively. |
| first_indexed | 2024-03-10T16:57:09Z |
| format | Article |
| id | doaj.art-b02546b04af84067b600a60984251bcb |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-10T16:57:09Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-b02546b04af84067b600a60984251bcb2023-11-20T11:06:09ZengBMCBMC Bioinformatics1471-21052023-09-0124112510.1186/s12859-023-05487-7Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heartSz-Wei Chu0Feng-Sheng Wang1Department of Chemical Engineering, National Chung Cheng UniversityDepartment of Chemical Engineering, National Chung Cheng UniversityAbstract In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco’s modified eagle medium (DMEM) and Ham’s medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham’s medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.https://doi.org/10.1186/s12859-023-05487-7Flux balance analysisGenome-scale metabolic modelConstraint-based modelingDrug discoveryHybrid differential evolutionMulti-level optimization |
| spellingShingle | Sz-Wei Chu Feng-Sheng Wang Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart BMC Bioinformatics Flux balance analysis Genome-scale metabolic model Constraint-based modeling Drug discovery Hybrid differential evolution Multi-level optimization |
| title | Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart |
| title_full | Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart |
| title_fullStr | Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart |
| title_full_unstemmed | Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart |
| title_short | Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart |
| title_sort | fuzzy optimization for identifying antiviral targets for treating sars cov 2 infection in the heart |
| topic | Flux balance analysis Genome-scale metabolic model Constraint-based modeling Drug discovery Hybrid differential evolution Multi-level optimization |
| url | https://doi.org/10.1186/s12859-023-05487-7 |
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