Joshua Pickard, Cooper Stansbury, Amit Surana, Anthony Bloch, Indika Rajapakse
{"title":"自适应系统的生物标志物选择","authors":"Joshua Pickard, Cooper Stansbury, Amit Surana, Anthony Bloch, Indika Rajapakse","doi":"arxiv-2405.09809","DOIUrl":null,"url":null,"abstract":"Biomarker selection and real-time monitoring of cell dynamics remains an\nactive challenge in cell biology and biomanufacturing. Here, we develop\nscalable adaptations of classic approaches to sensor selection for biomarker\nidentification on several transcriptomics and biological datasets that are\notherwise cannot be studied from a controls perspective. To address challenges\nin system identification of biological systems and provide robust biomarkers,\nwe propose Dynamic and Structure Guided Sensors Selection (DSS and SGSS),\nmethods by which temporal models and structural experimental data can be used\nto supplement traditional approaches to sensor selection. These approaches\nleverage temporal models and experimental data to enhance traditional sensor\nselection techniques. Unlike conventional methods that assume well-known, fixed\ndynamics, DSS and SGSS adaptively select sensors that maximize observability\nwhile accounting for the time-varying nature of biological systems.\nAdditionally, they incorporate structural information to identify robust\nsensors even in cases where system dynamics are poorly understood. We validate\nthese two approaches by performing estimation on several high dimensional\nsystems derived from temporal gene expression data from partial observations.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Biomarker Selection for Adaptive Systems\",\"authors\":\"Joshua Pickard, Cooper Stansbury, Amit Surana, Anthony Bloch, Indika Rajapakse\",\"doi\":\"arxiv-2405.09809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biomarker selection and real-time monitoring of cell dynamics remains an\\nactive challenge in cell biology and biomanufacturing. Here, we develop\\nscalable adaptations of classic approaches to sensor selection for biomarker\\nidentification on several transcriptomics and biological datasets that are\\notherwise cannot be studied from a controls perspective. To address challenges\\nin system identification of biological systems and provide robust biomarkers,\\nwe propose Dynamic and Structure Guided Sensors Selection (DSS and SGSS),\\nmethods by which temporal models and structural experimental data can be used\\nto supplement traditional approaches to sensor selection. These approaches\\nleverage temporal models and experimental data to enhance traditional sensor\\nselection techniques. Unlike conventional methods that assume well-known, fixed\\ndynamics, DSS and SGSS adaptively select sensors that maximize observability\\nwhile accounting for the time-varying nature of biological systems.\\nAdditionally, they incorporate structural information to identify robust\\nsensors even in cases where system dynamics are poorly understood. We validate\\nthese two approaches by performing estimation on several high dimensional\\nsystems derived from temporal gene expression data from partial observations.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"23 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-16\",\"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-2405.09809\",\"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-2405.09809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Biomarker selection and real-time monitoring of cell dynamics remains an
active challenge in cell biology and biomanufacturing. Here, we develop
scalable adaptations of classic approaches to sensor selection for biomarker
identification on several transcriptomics and biological datasets that are
otherwise cannot be studied from a controls perspective. To address challenges
in system identification of biological systems and provide robust biomarkers,
we propose Dynamic and Structure Guided Sensors Selection (DSS and SGSS),
methods by which temporal models and structural experimental data can be used
to supplement traditional approaches to sensor selection. These approaches
leverage temporal models and experimental data to enhance traditional sensor
selection techniques. Unlike conventional methods that assume well-known, fixed
dynamics, DSS and SGSS adaptively select sensors that maximize observability
while accounting for the time-varying nature of biological systems.
Additionally, they incorporate structural information to identify robust
sensors even in cases where system dynamics are poorly understood. We validate
these two approaches by performing estimation on several high dimensional
systems derived from temporal gene expression data from partial observations.