B. Sprague, M. Gardner, C. Pearson, P. Maddox, K. Bloom, E. Salmon, D. Odde
{"title":"模型-卷积方法模拟荧光蛋白动力学","authors":"B. Sprague, M. Gardner, C. Pearson, P. Maddox, K. Bloom, E. Salmon, D. Odde","doi":"10.1109/ACSSC.2004.1399478","DOIUrl":null,"url":null,"abstract":"Fluorescence microscopy is a popular technique for visualizing protein dynamics in living cells. However, the precise distribution of fluorophores underlying the observed fluorescence is not always obvious, even after deconvolution, particularly when features on a scale of 250 nm or less are of interest In contrast, quantitative models of protein dynamics predict an actual fluorophore distribution. \"Model-convolution\" is a method that bridges this gap by convolving model-predicted fluorophore location data with the point spread function of the microscope system so that simulated images can be generated and directly compared to experimental images. This article offers a practical guide to model-convolution.","PeriodicalId":396779,"journal":{"name":"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Model-convolution approach to modeling fluorescent protein dynamics\",\"authors\":\"B. Sprague, M. Gardner, C. Pearson, P. Maddox, K. Bloom, E. Salmon, D. Odde\",\"doi\":\"10.1109/ACSSC.2004.1399478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fluorescence microscopy is a popular technique for visualizing protein dynamics in living cells. However, the precise distribution of fluorophores underlying the observed fluorescence is not always obvious, even after deconvolution, particularly when features on a scale of 250 nm or less are of interest In contrast, quantitative models of protein dynamics predict an actual fluorophore distribution. \\\"Model-convolution\\\" is a method that bridges this gap by convolving model-predicted fluorophore location data with the point spread function of the microscope system so that simulated images can be generated and directly compared to experimental images. This article offers a practical guide to model-convolution.\",\"PeriodicalId\":396779,\"journal\":{\"name\":\"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.2004.1399478\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2004.1399478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model-convolution approach to modeling fluorescent protein dynamics
Fluorescence microscopy is a popular technique for visualizing protein dynamics in living cells. However, the precise distribution of fluorophores underlying the observed fluorescence is not always obvious, even after deconvolution, particularly when features on a scale of 250 nm or less are of interest In contrast, quantitative models of protein dynamics predict an actual fluorophore distribution. "Model-convolution" is a method that bridges this gap by convolving model-predicted fluorophore location data with the point spread function of the microscope system so that simulated images can be generated and directly compared to experimental images. This article offers a practical guide to model-convolution.