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  A benchmark for methods in reverse engineering and model discrimination : problem formulation and solutions

Kremling, A., Kremling, S., Gadkar, K., Doyle, F. J., Sauter, T., Bullinger, E., et al. (2004). A benchmark for methods in reverse engineering and model discrimination: problem formulation and solutions. Genome Research, 14(9), 1773-1785. doi:10.1101/gr.1226004.

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Kremling, A.1, Author           
Kremling, Sophia1, Author           
Gadkar, K.2, Author
Doyle, F. J.2, Author
Sauter, T.1, Author           
Bullinger, E.3, Author
Allgoewer, F.3, Author
Gilles, E. D.1, Author           
Affiliations:
1Systems Biology, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738155              
2University of California-Santa Barbara, USA, ou_persistent22              
3Universität Stuttgart, ou_persistent22              

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 Abstract: A benchmark problem is described for the reconstruction and analysis of biochemical networks given sampled experimental data. The growth of the organisms is described in a bio-reactor where one substrate is fed into the reactor with a given feed rate and feed concentration. Measurements for some intracellular components are provided representing a small biochemical network. Problems of reverse engineering, parameter estimation, and identifiability are addressed. The contribution mainly focuses on the problem of model discrimination. If two or more model variants describe the available experimental data, a new experiment must be designed to discriminate between the hypothetical models. For the problem presented the feed rate and feed concentration of a bioreactor system are available as control inputs. To verify calculated input profiles an interactive web-site http://www.sysbio.de/projects/benchmark/ is provided. Several solutions based on linear and nonlinear models are discussed. Copyright © 2015 by Cold Spring Harbor Laboratory Press [accessed 2015 July 8]

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Language(s): eng - English
 Dates: 2004
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 207955
Other: 20/04
DOI: 10.1101/gr.1226004
 Degree: -

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Title: Genome Research
Source Genre: Journal
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Pages: - Volume / Issue: 14 (9) Sequence Number: - Start / End Page: 1773 - 1785 Identifier: -