A Multi-Model Approach to Identification of Biosynthetic Pathways
Abstract
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.
Additional Information
© 2007 IEEE. Issue Date: 9-13 July 2007; Date of Current Version: 30 July 2007. Research supported in part by the Institute for Collaborative Biotechnologies through grant DAAD19-03-D-0004 from the U.S. Army Research Office.Attached Files
Published - Dunlop2007p86802009_American_Control_Conference_Vols_1-9.pdf
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Additional details
- Eprint ID
- 19682
- Resolver ID
- CaltechAUTHORS:20100827-104901767
- Army Research Office (ARO)
- DAAD19-03-D-0004
- Created
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2010-09-01Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field