Abstract
Modern biochemistry opens new perspectives in understanding and finding remedies for diseases like cancer, diabetes or Alzheimer’s, where regulatory mechanisms of cells in an organism’s metabolism fail. This is made possible due to broad and highly specialized knowledge in biochemical contexts, obtained by computer-based simulations of diverse cell and enzyme interactions.
This work focuses on the simulation of such interactions via the rule-based method. Herein, the behavior of complex biochemical process in a system is split into several reoccuring patterns, to be completely modeled and simulated by the use of pattern matching tools and the according model transformations.
Already existent and well-established specifications such as Kappa or the BioNetGenLanguage provide extensive possibilities to model such systems and simulations employing domain-specific languages. Still, these have issues in terms of their intuitive comprehensibility and general usability nonetheless. In regard to those parameters a specification for such rule-based simulations and a corresponding framework for integrating it into the already existent simulation tool SimSG is developed and implemented. Finally this new language is evaluated with respect to the intended optimization of the given aspects and the pattern matching tools used for simulation are compared based on different models of various types.
Tobias Niehues
PhD Student in Cognitive Science
My research interests include statistical modeling of human behavior, especially regarding human decision-making and (the interplay of) perception & action.