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Nucleic Acids Research Advance Access originally published online on February 2, 2009
Nucleic Acids Research 2009 37(5):e38; doi:10.1093/nar/gkp022
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Nucleic Acids Research, 2009, Vol. 37, No. 5 e38
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

Model-based redesign of global transcription regulation

Javier Carrera1,2, Guillermo Rodrigo1 and Alfonso Jaramillo3,4,*

1Instituto de Biologia Molecular y Celular de Plantas, CSIC, 2Instituto de Aplicaciones en Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA), Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain, 3Laboratoire de Biochimie, Ecole Polytechnique - CNRS, Route de Saclay, 91128 Palaiseau Cedex and 4Epigenomics Project, Universite d'Evry Val d'Essonne - Genopole - CNRS, 523 Terrasses de l' Agora, 91034 Evry Cedex, France

*To whom correspondence should be addressed. Tel: +33 1 69474444; Fax: +33 1 69474437; Email: alfonso.jaramillo{at}polytechnique.fr

Received July 12, 2008. Revised January 2, 2009. Accepted January 7, 2009.

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology.

The authors wish it to be known that, in their opinion, the first two authors have contributed equally to this work.


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