Modeling Microorganisms

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: March 2017
• Date (Start): 12. October 2016
• Date (End): 23. March 2017
• Matrikelnummer: 1226847
• First Supervisor: Ivan Viola

Abstract

Modeling of microorganisms is a cumbersome task, when biologists want to create a visual representation of a certain microorganism. To correctly model structures on atomic resolution, each molecule (for example proteins and lipids) has to be placed at its correct position. For microorganisms of larger dimensions the modeling process takes a very long time, at this point an improved modeling approach is required. It would be possible to create a rule-based modeling approach, but usually rules restrict the final outcome and produces repeating patterns. Therefore a tool that places molecules based on statistical evaluations and foresight was the main idea behind this project. The tool should allow for modeling complex organisms on molecular resolution by placing a minimal amount of examples and generalizing similar entities. A decision tree as learning structure evaluates the user’s actions, learns from them and reorganizes the whole structure. With this approach the user should be able to model complex cellular structures in as few steps as possible, also more complex actions, such as orientation towards a certain reference point, clusters, varying distribution united a real-time editor should improve the modeling task significantly.

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BibTeX

@bachelorsthesis{Mitterhofer_2017,
  title =      "Modeling Microorganisms",
  author =     "Lukas Mitterhofer",
  year =       "2017",
  abstract =   "Modeling of microorganisms is a cumbersome task, when
               biologists want to create a visual representation of a
               certain microorganism. To correctly model structures on
               atomic resolution, each molecule (for example proteins and
               lipids) has to be placed at its correct position. For
               microorganisms of larger dimensions the modeling process
               takes a very long time, at this point an improved modeling
               approach is required. It would be possible to create a
               rule-based modeling approach, but usually rules restrict the
               final outcome and produces repeating patterns. Therefore a
               tool that places molecules based on statistical evaluations
               and foresight was the main idea behind this project. The
               tool should allow for modeling complex organisms on
               molecular resolution by placing a minimal amount of examples
               and generalizing similar entities. A decision tree as
               learning structure evaluates the user’s actions, learns
               from them and reorganizes the whole structure. With this
               approach the user should be able to model complex cellular
               structures in as few steps as possible, also more complex
               actions, such as orientation towards a certain reference
               point, clusters, varying distribution united a real-time
               editor should improve the modeling task significantly.",
  month =      mar,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2017/Mitterhofer_2017/",
}