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A Multi-Model Approach to Identification of Biosynthetic Pathways
Abstract We present an identification framework for …
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.
lect the one that best describes the data.  +
Authors Mary J Dunlop, Elisa Franco, Richard M Murray  +
ID 2006x  +
Source American Control Conference (ACC)  +
Tag dfm07-acc  +
Title A Multi-Model Approach to Identification of Biosynthetic Pathways +
Type Preprint  +
Categories Papers
Modification date
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15 May 2016 06:17:26  +
URL
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http://www.cds.caltech.edu/~murray/preprints/dfm07-acc.pdf  +
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A Multi-Model Approach to Identification of Biosynthetic Pathways + Title
 

 

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