Inferring system properties from thermodynamic fluctuations : a tool development approach
Name
1241733258-MIT.pdf
Size
9.39 MB
Format
Adobe PDF
Checksum (MD5)
d2cd17957e263ab1ecb0c0504834a493
Author(s)
Jung, Yoon,Ph. D.Massachusetts Institute of Technology.
Advisor(s)
Nikta Fakhri.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Biological systems are far from equilibrium which require novel tools for unraveling their complex behavior. This thesis focuses on developing a toolbox in order to understand properties of living systems from thermodynamic fluctuations. In the first chapter, I discuss a fluorescence imaging platform which allows 3D information combined with non-invasive and photostable probes named single-walled carbon nanotubes. The second chapter discusses an image processing algorithm for analyzing the fluorescence images acquired with the proposed custom-built microscope. I demonstrate its robust image reconstruction capability under dense scenes of fluorescence images with its inherent parallel nature which allows implementation on GPUs. Finally, I develop a framework which predicts system properties from thermodynamic fluctuations in a data-driven manner. The proposed framework uses feature extraction methods based on wavelets with recurrent neural networks for processing time series data. A combination of these tools completes a pipeline which allows studying complex behavior of biological systems.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, May, 2020
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-70).
Subjects
Physics.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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