Inferring system properties from thermodynamic fluctuations : a tool development approach

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Physics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Jung, Yoon,Ph. D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Nikta Fakhri.

• Physics.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130216

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130216