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Work in the Hahn
lab involves a model reduction framework for systems describing phenomena
occurring at two time scales. The model reduction procedure reduces the
complexity of the model at both time scales simultaneously, while it ensures
that the mechanisms that link the time scales are still accurately described
in the reduced model. The procedure also ensures that the reduced model
captures the sensitivity of the system to changes in the model parameters.
Model reduction algorithms are used for developing an improved understanding
of biological systems.
As model reduction is commonly performed to reduce the size and complexity of
existing models with known accuracy, it is important to point out the properties
that set the ongoing work apart from other approaches.
Specifically, this model
reduction procedure is
1) geared towards nonlinear systems, which is important
for biological systems;
2) performed for systems including phenomena at two time
scales, where the reduction does not just separately focus on each time scale
but takes the interactions into account;
3) specifically addressing parametric
sensitivity;
4) directly incorporated into the modeling process and can provide
information about parts of the model to be reduced as well as about components
which may benefit from further refinement; and
5) used for analyzing important
dynamic cellular phenomena.
