{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/130216"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/130216","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Inferring system properties from thermodynamic fluctuations : a tool development approach","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Jung, Yoon,Ph. D.Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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