Model Reduction of Rule-based ODE Models: Exact vs. Approximate Approach

Model Reduction of Rule-based ODE Models: Exact vs. Approximate Approach

Holger Conzelmann, Harvard Medical School

The problem of combinatorial complexity strongly restricts the applicability of rule-based ODE modeling to large signaling networks that would consist of billions of species and reactions. One possibility to tackle this problem is systematic model reduction. In literature a number of different approaches are discussed which basically can be classified in exact and approximate approaches. Using exact reduction methods, of course, is most desirable since one can prove that the reduced model has exactly the same input / output behavior as the complete one. However, these exact methods are not applicable to all kinds of combinatorial reaction networks but have certain requirements. The approximate reduction approach, on the other side, is applicable to a much larger class of systems, however, one only can give vague estimates of the approximation quality. In my talk I will give an overview about the existing methods with a focus on the discussion of applicability requirements as well as drawbacks of each method.