Format-Preserving Reduction of Canonical Nonlinear Models
by Eberhard O. Voit
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New experimental techniques in biology have been generating unprecedented amounts of data. These offer new opportunities for analysis, including the design, analysis, and application of computational models, which are usually formulated as systems of differential equations. While this trend is very welcome, it brings with it challenges associated with technical and conceptual aspects of the models and their analysis. The article proposes methods for reductions in the size of models that approximately retain their dynamical responses. These approximations are often so good that errors are within the range of experimental uncertainty. The proposed methods are hoped to tame some of the challenges associated with increasingly larger models.
Reduction of a Model of White Rot Fungus
