tetano
Editor, Senior Moderator
BMC Bioinformatics
. 2023 Sep 27;24(1):364.
doi: 10.1186/s12859-023-05487-7. Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart
Sz-Wei Chu[SUP] 1 [/SUP], Feng-Sheng Wang[SUP] 2 [/SUP]
Affiliations
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.
Keywords: Constraint-based modeling; Drug discovery; Flux balance analysis; Genome-scale metabolic model; Hybrid differential evolution; Multi-level optimization.
. 2023 Sep 27;24(1):364.
doi: 10.1186/s12859-023-05487-7. Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart
Sz-Wei Chu[SUP] 1 [/SUP], Feng-Sheng Wang[SUP] 2 [/SUP]
Affiliations
- PMID: 37759157
- DOI: 10.1186/s12859-023-05487-7
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.
Keywords: Constraint-based modeling; Drug discovery; Flux balance analysis; Genome-scale metabolic model; Hybrid differential evolution; Multi-level optimization.