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A Multi-Model Approach to Identification of Biosynthetic Pathways

Dunlop, Mary J. and Franco, Elisa and Murray, Richard M. (2007) A Multi-Model Approach to Identification of Biosynthetic Pathways. In: American Control Conference, 2007. ACC '07. IEEE , New York, NY, pp. 1600-1605. ISBN 1-4244-0988-8.

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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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Franco, Elisa0000-0003-1103-2668
Murray, Richard M.0000-0002-5785-7481
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.
Funding AgencyGrant Number
Army Research Office (ARO)DAAD19-03-D-0004
Record Number:CaltechAUTHORS:20100827-104901767
Persistent URL:
Official Citation:Dunlop, M.J.; Franco, E.; Murray, R.M.; , "A Multi-Model Approach to Identification of Biosynthetic Pathways," American Control Conference, 2007. ACC '07 , vol., no., pp.1600-1605, 9-13 July 2007 doi: 10.1109/ACC.2007.4282720 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:19682
Deposited By: Jason Perez
Deposited On:01 Sep 2010 16:23
Last Modified:08 Nov 2021 23:54

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