2019 Research in the Capitol
Presentation Type
Open Access Poster Presentation
Keywords
Fluorescence microscopy; Biomolecules;
Abstract
Single-Molecule studies use advanced microscopy techniques to view biomolecules, such as proteins and DNA, individually. On a slide, fluorescently-labeled molecules are immobilized and imaged using lasers, and the patterns of fluorescence can give important information about the interactions of multiple molecules. To extract this information, advanced, customizable data analysis tools must be created. The first goal is to create a method to robustly normalize (correct for brightness) single-channel fluorescence data. The second goal is to extend pattern recognition of binding order to multi-state and multi-channel binding patterns. The KERA 3.0 suite links creative pattern-recognition and normalization techniques with the abilities of exiting idealization software to extract this information from previously intractable data. This allows researchers to study protein complexes, their inhibitors, and their mechanisms, more holistically and more efficiently.
Start Date
1-4-2019 11:00 AM
End Date
1-4-2019 2:30 PM
Event Host
University Honors Programs, Iowa Regent Universities
Faculty Advisor
Seyed Tabei
Department
Department of Physics
Copyright
©2019 Joseph Tibbs, Elizabeth Boehm, Wayne Bowie, Todd Washington, Maria Spies, and Ali Tabei
File Format
application/pdf
Recommended Citation
Tibbs, Joseph; Boehm, Elizabeth; Bowie, Wayne; Washington, Todd; Spies, Maria; and Tabei, Ali, "Novel Data Analysis Methods in Multi-Channel and Multi-State Binding Experiments" (2019). Research in the Capitol. 6.
https://scholarworks.uni.edu/rcapitol/2019/all/6
Novel Data Analysis Methods in Multi-Channel and Multi-State Binding Experiments
Single-Molecule studies use advanced microscopy techniques to view biomolecules, such as proteins and DNA, individually. On a slide, fluorescently-labeled molecules are immobilized and imaged using lasers, and the patterns of fluorescence can give important information about the interactions of multiple molecules. To extract this information, advanced, customizable data analysis tools must be created. The first goal is to create a method to robustly normalize (correct for brightness) single-channel fluorescence data. The second goal is to extend pattern recognition of binding order to multi-state and multi-channel binding patterns. The KERA 3.0 suite links creative pattern-recognition and normalization techniques with the abilities of exiting idealization software to extract this information from previously intractable data. This allows researchers to study protein complexes, their inhibitors, and their mechanisms, more holistically and more efficiently.
