Two scenarios for overcoming drug resistance by co-targeting
Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of...
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| Vydáno v: | International journal of bioinformatics research and applications Ročník 11; číslo 1; s. 72 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
2015
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| ISSN: | 1744-5485 |
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| Abstract | Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively. |
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| AbstractList | Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively. Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively. |
| Author | Taheri, Golnaz Ayati, Marzieh Wong, Limsoon Eslahchi, Changiz |
| Author_xml | – sequence: 1 givenname: Golnaz surname: Taheri fullname: Taheri, Golnaz organization: School of Mathematics and Computer Science and Center of Excellence in Bioinformatics, College of Science, University of Tehran, Tehran, Iran; Institute for Research in Fundamental Sciences (IPM), Tehran, Iran – sequence: 2 givenname: Marzieh surname: Ayati fullname: Ayati, Marzieh organization: Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA – sequence: 3 givenname: Limsoon surname: Wong fullname: Wong, Limsoon organization: Department of Computer Science, National University of Singapore, Singapore – sequence: 4 givenname: Changiz surname: Eslahchi fullname: Eslahchi, Changiz organization: Department of Computer Science, Shahid Beheshti University G.C., Tehran, Iran |
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| CitedBy_id | crossref_primary_10_1016_j_asoc_2020_106202 crossref_primary_10_1016_j_inffus_2024_102485 crossref_primary_10_1016_j_asoc_2022_109510 |
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| Keywords | protein protein interaction balanced bipartitioning E coli pathogens C jejuni bioinformatics PPI networks drug targets essential pathways drug resistance co–targeting |
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| SubjectTerms | Anti-Bacterial Agents - pharmacology Bacterial Proteins - metabolism Computer Simulation Drug Design Drug Resistance, Bacterial - drug effects Drug Resistance, Bacterial - physiology Models, Biological Protein Interaction Mapping - methods Signal Transduction - drug effects Signal Transduction - physiology |
| Title | Two scenarios for overcoming drug resistance by co-targeting |
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