Department of Computer Science
University of Rostock
Since April 2000 Dr. Uhrmacher has been as Associate Professor and Head of the Modeling and Simulation Group of the Department of Computer Science, University of Rostock. Diploma (1987) at the Faculty of Computer Science, University of Koblenz, Research Scientist (1987-1992) at the Environmental Systems Research Laboratory, University of Kassel, (1991-1992) Fellow of the Otto Brown Foundation, (1992) Ph.D. at the Department of Computer Science, University of Koblenz, (1992-1994) Research Scientist at the Artificial Intelligence and Simulation Laboratory, University of Arizona Tucson, (1993-1994) Collaborator of the Biosphere Space Ventures Incorporation Tucson, Arizona, (1993-1994) Fellow of the Feodor Lynen Program, Alexander von Humboldt Foundation, (1994-2000) Scientific Assistant (Assistant Professor) at the AI Department of the Faculty of Computer Science, University of Ulm, (2000) Habilitation (Venia Legendi) at the Faculty of Computer Science, University of Ulm.
Editor-in-Chief of the journal Simulation: Transactions of the Society for Modeling and Computer Simulation International, and Associate Editor of the journal Transactions on Computational Systems Biology.
Agent-Oriented Simulation - Applications and Challenges
The relations between agents and simulations are manifold. Different application projects shall illuminate the interrelation. Agents are used as a metaphor for modeling dynamic systems, e.g. in sociological, ecological and (cell-) biological applications. Simulation is used to test software agents in virtual dynamic environments and thus become part of agent-oriented software engineering. Testing software designed to help people suffering from dementia in their daily life or evaluating strategies to provide services in ad hoc networks require human behavior representations, however at quite different levels of detail. The heterogeneity of models, their number, and their dynamics which typically include dynamic interaction, composition - and behavior pattern add to the complexity of models and provide challenges for modeling, and simulation alike.
Multi-Level Models in Systems Biology
Different modeling and simulation methods are applied in Systems Biology. They emphasize a qualitative, quantitative, deterministic, stochastic, continuous or discrete view on the system under study. A less explored dimension is the organizational level at which the system is studied. Are we only interested in concentrations or are we interested in the activities of individual actors in the cell? Typically, the later is combined with a discrete-stochastic perception. Multi-level models combine macro and micro perspectives and allow a zooming in and out on demand. To support multi-level models challenges current methods. How different modeling formalisms and simulation engines respond to these challenges shall be discussed in this lecture.