A Multi-Model Approach to Identification of Biosynthetic Pathways
Mary J Dunlop, Elisa Franco, Richard M Murray
American Control Conference (ACC)
We present an identification framework for biochemical systems that allows multiple candidate models to be compared. This framework is designed to select a model that fits the data while maintaining model simplicity. The model identification task is divided into a parameter estimation stage and a model comparison stage. Model selection is based on calculating Akaike's Information Criterion, which is a systematic method for determining the model that best represents a set of experimental data. Two case studies are presented: a simulated transcriptional control circuit and a system of oscillators that has been built and characterized in vitro. In both examples the multi-model framework is able to discriminate between model candidates to select the one that best describes the data.
- Preprint: http://www.cds.caltech.edu/~murray/preprints/dfm07-acc.pdf