Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza
{"title":"基于嵌入式的 Wnt 信号传递反应网络比较","authors":"Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza","doi":"arxiv-2404.06515","DOIUrl":null,"url":null,"abstract":"This work introduces a new method for comparing two reaction networks of the\nsame or closely related systems through their embedded networks in terms of the\nshared set of species. Hence, we call this method the Common Species Embedded\nNetworks (CSEN) analysis. Using this approach, we conduct a comparison of\nexisting reaction networks associated with Wnt signaling models (Lee, Schmitz,\nMacLean, and Feinberg) that we have identified. The analysis yields three\nimportant results for these Wnt models. First, the CSEN analysis of the Lee\n(mono-stationary) and Feinberg (multi-stationary) shows a strong similarity,\njustifying the study of the Feinberg model, which was a modified Lee model\nconstructed to study an important network property called \"concordance\". It\nalso challenge the absoluteness of discrimination of the models into\nmono-stationarity versus multi-stationarity, which is a main result of Maclean\net al. (PNAS USA 2015). Second, the CSEN analysis provides evidence supporting\na strong similarity between the Schmitz and MacLean models, as indicated by the\n\"proximate equivalence\" that we have identified. Third, the analysis\nunderscores the absence of a comparable relationship between the Feinberg and\nMacLean models, highlighting distinctive differences between the two. Thus, our\napproach could be a useful tool to compare mathematical models of the same or\nclosely related systems.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"47 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Embedding-based comparison of reaction networks of Wnt signaling\",\"authors\":\"Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza\",\"doi\":\"arxiv-2404.06515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work introduces a new method for comparing two reaction networks of the\\nsame or closely related systems through their embedded networks in terms of the\\nshared set of species. Hence, we call this method the Common Species Embedded\\nNetworks (CSEN) analysis. Using this approach, we conduct a comparison of\\nexisting reaction networks associated with Wnt signaling models (Lee, Schmitz,\\nMacLean, and Feinberg) that we have identified. The analysis yields three\\nimportant results for these Wnt models. First, the CSEN analysis of the Lee\\n(mono-stationary) and Feinberg (multi-stationary) shows a strong similarity,\\njustifying the study of the Feinberg model, which was a modified Lee model\\nconstructed to study an important network property called \\\"concordance\\\". It\\nalso challenge the absoluteness of discrimination of the models into\\nmono-stationarity versus multi-stationarity, which is a main result of Maclean\\net al. (PNAS USA 2015). Second, the CSEN analysis provides evidence supporting\\na strong similarity between the Schmitz and MacLean models, as indicated by the\\n\\\"proximate equivalence\\\" that we have identified. Third, the analysis\\nunderscores the absence of a comparable relationship between the Feinberg and\\nMacLean models, highlighting distinctive differences between the two. Thus, our\\napproach could be a useful tool to compare mathematical models of the same or\\nclosely related systems.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"47 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Molecular Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2404.06515\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2404.06515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Embedding-based comparison of reaction networks of Wnt signaling
This work introduces a new method for comparing two reaction networks of the
same or closely related systems through their embedded networks in terms of the
shared set of species. Hence, we call this method the Common Species Embedded
Networks (CSEN) analysis. Using this approach, we conduct a comparison of
existing reaction networks associated with Wnt signaling models (Lee, Schmitz,
MacLean, and Feinberg) that we have identified. The analysis yields three
important results for these Wnt models. First, the CSEN analysis of the Lee
(mono-stationary) and Feinberg (multi-stationary) shows a strong similarity,
justifying the study of the Feinberg model, which was a modified Lee model
constructed to study an important network property called "concordance". It
also challenge the absoluteness of discrimination of the models into
mono-stationarity versus multi-stationarity, which is a main result of Maclean
et al. (PNAS USA 2015). Second, the CSEN analysis provides evidence supporting
a strong similarity between the Schmitz and MacLean models, as indicated by the
"proximate equivalence" that we have identified. Third, the analysis
underscores the absence of a comparable relationship between the Feinberg and
MacLean models, highlighting distinctive differences between the two. Thus, our
approach could be a useful tool to compare mathematical models of the same or
closely related systems.