{"title":"生物网络的稳定性和脆弱性:真核模式生物酿酒酵母","authors":"Volkan Altuntas, Murat Gök","doi":"10.1109/UBMK.2017.8093575","DOIUrl":null,"url":null,"abstract":"Recent studies of biological networks show that these networks are robust against the random or selective deletion of network nodes and / or edges. Ability to maintain performance of network under mutations is a key feature of live systems that has long been recognized. However, the molecular and cellular basis of this stability has just begun to be understood. Robustness is a key to understanding cellular complexity, illuminating design principles, and encouraging closer interaction between experiment and theory. A biological network mutation can be defined as the creation of a new network with k allowed network change operations for a given G network. While mutating the network, our goal is to observe the change in the measured distance estimate value after k changes of the defined distance measurement method M. In this study, the effects of edge deletion and edge insertion mutations on network topology and diffusion-based function estimation algorithms are investigated by using random mutation model on the protein-protein interaction network of eukaryote Saccharomyces cerevisiae yeast, containing 5936 nodes and 65139 edges. Experimental results shows that Saccharomyces cerevisiae protein-protein interaction network has high robustness against random mutations and that the generated mutations have no significant effect on network topology and estimation techniques.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The stability and fragility of biological networks: Eukaryotic model organism Saccharomyces cerevisiae\",\"authors\":\"Volkan Altuntas, Murat Gök\",\"doi\":\"10.1109/UBMK.2017.8093575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent studies of biological networks show that these networks are robust against the random or selective deletion of network nodes and / or edges. Ability to maintain performance of network under mutations is a key feature of live systems that has long been recognized. However, the molecular and cellular basis of this stability has just begun to be understood. Robustness is a key to understanding cellular complexity, illuminating design principles, and encouraging closer interaction between experiment and theory. A biological network mutation can be defined as the creation of a new network with k allowed network change operations for a given G network. While mutating the network, our goal is to observe the change in the measured distance estimate value after k changes of the defined distance measurement method M. In this study, the effects of edge deletion and edge insertion mutations on network topology and diffusion-based function estimation algorithms are investigated by using random mutation model on the protein-protein interaction network of eukaryote Saccharomyces cerevisiae yeast, containing 5936 nodes and 65139 edges. Experimental results shows that Saccharomyces cerevisiae protein-protein interaction network has high robustness against random mutations and that the generated mutations have no significant effect on network topology and estimation techniques.\",\"PeriodicalId\":201903,\"journal\":{\"name\":\"2017 International Conference on Computer Science and Engineering (UBMK)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Computer Science and Engineering (UBMK)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UBMK.2017.8093575\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computer Science and Engineering (UBMK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UBMK.2017.8093575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The stability and fragility of biological networks: Eukaryotic model organism Saccharomyces cerevisiae
Recent studies of biological networks show that these networks are robust against the random or selective deletion of network nodes and / or edges. Ability to maintain performance of network under mutations is a key feature of live systems that has long been recognized. However, the molecular and cellular basis of this stability has just begun to be understood. Robustness is a key to understanding cellular complexity, illuminating design principles, and encouraging closer interaction between experiment and theory. A biological network mutation can be defined as the creation of a new network with k allowed network change operations for a given G network. While mutating the network, our goal is to observe the change in the measured distance estimate value after k changes of the defined distance measurement method M. In this study, the effects of edge deletion and edge insertion mutations on network topology and diffusion-based function estimation algorithms are investigated by using random mutation model on the protein-protein interaction network of eukaryote Saccharomyces cerevisiae yeast, containing 5936 nodes and 65139 edges. Experimental results shows that Saccharomyces cerevisiae protein-protein interaction network has high robustness against random mutations and that the generated mutations have no significant effect on network topology and estimation techniques.