{"title":"微管种群统计特性的测定。","authors":"Tyson DiLorenzo, Lee Ligon, Donald Drew","doi":"10.4236/am.2016.713125","DOIUrl":null,"url":null,"abstract":"<p><p>Microtubules are structures within the cell that form a transportation network along which motor proteins tow cargo to destinations. To establish and maintain a structure capable of serving the cell's tasks, microtubules undergo deconstruction and reconstruction regularly. This change in structure is critical to tasks like wound repair and cell motility. Images of fluorescing microtubule networks are captured in grayscale at different wavelengths, displaying different tagged proteins. The analysis of these polymeric structures involves identifying the presence of the protein and the direction of the structure in which it resides. This study considers the problem of finding statistical properties of sections of microtubules. We consider the research done on directional filters and utilize a basic solution to find the center of a ridge. The method processes the captured image by centering a circle around pre-determined pixel locations so that the highest possible average pixel intensity is found within the circle, thus marking the center of the microtubule. The location of these centers allows us to estimate angular direction and curvature of the microtubules, statistically estimate the direction of microtubules in a region of the cell, and compare properties of different types of microtubule networks in the same region. To verify accuracy, we study the results of the method on a test image.</p>","PeriodicalId":64940,"journal":{"name":"应用数学(英文)","volume":"7 13","pages":"1456-1475"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528678/pdf/","citationCount":"0","resultStr":"{\"title\":\"Determination of Statistical Properties of Microtubule Populations.\",\"authors\":\"Tyson DiLorenzo, Lee Ligon, Donald Drew\",\"doi\":\"10.4236/am.2016.713125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Microtubules are structures within the cell that form a transportation network along which motor proteins tow cargo to destinations. To establish and maintain a structure capable of serving the cell's tasks, microtubules undergo deconstruction and reconstruction regularly. This change in structure is critical to tasks like wound repair and cell motility. Images of fluorescing microtubule networks are captured in grayscale at different wavelengths, displaying different tagged proteins. The analysis of these polymeric structures involves identifying the presence of the protein and the direction of the structure in which it resides. This study considers the problem of finding statistical properties of sections of microtubules. We consider the research done on directional filters and utilize a basic solution to find the center of a ridge. The method processes the captured image by centering a circle around pre-determined pixel locations so that the highest possible average pixel intensity is found within the circle, thus marking the center of the microtubule. The location of these centers allows us to estimate angular direction and curvature of the microtubules, statistically estimate the direction of microtubules in a region of the cell, and compare properties of different types of microtubule networks in the same region. To verify accuracy, we study the results of the method on a test image.</p>\",\"PeriodicalId\":64940,\"journal\":{\"name\":\"应用数学(英文)\",\"volume\":\"7 13\",\"pages\":\"1456-1475\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528678/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"应用数学(英文)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4236/am.2016.713125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2016/8/15 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"应用数学(英文)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/am.2016.713125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/8/15 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Determination of Statistical Properties of Microtubule Populations.
Microtubules are structures within the cell that form a transportation network along which motor proteins tow cargo to destinations. To establish and maintain a structure capable of serving the cell's tasks, microtubules undergo deconstruction and reconstruction regularly. This change in structure is critical to tasks like wound repair and cell motility. Images of fluorescing microtubule networks are captured in grayscale at different wavelengths, displaying different tagged proteins. The analysis of these polymeric structures involves identifying the presence of the protein and the direction of the structure in which it resides. This study considers the problem of finding statistical properties of sections of microtubules. We consider the research done on directional filters and utilize a basic solution to find the center of a ridge. The method processes the captured image by centering a circle around pre-determined pixel locations so that the highest possible average pixel intensity is found within the circle, thus marking the center of the microtubule. The location of these centers allows us to estimate angular direction and curvature of the microtubules, statistically estimate the direction of microtubules in a region of the cell, and compare properties of different types of microtubule networks in the same region. To verify accuracy, we study the results of the method on a test image.