Model reduction in complex biochemical networksAn important tool in the analysis of complex physical phenomena is the
simulation of the underlying mathematical models, which are often given
by systems of ordinary and/or partial differential equations. As one
is interested in models as accurate as possible, linear models are often
insufficient such that one is faced with large-scale nonlinear systems.
Frequently, these cannot be handled efficiently, necessitating model order
reduction, i.e., the construction of a smaller system approximating the
original one. In this thesis, a recently introduced approach for nonlinear model reduction should be implemented and tested
by means of a real-life application arising in the context of biochemical reaction networks.
Contact:
Tobias Breiten