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Nucleic Acids Research Advance Access published online on December 1, 2007

Nucleic Acids Research, doi:10.1093/nar/gkm1034
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© 2007 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.


Nucleic Acid Enzymes

Ab initio thermodynamic modeling of distal multisite transcription regulation

Leonor Saiz and Jose M. G. Vilar*

Integrative Biological Modeling Laboratory, Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA

*To whom correspondence should be addressed. Tel: +1 646 888 2603; Fax: +1 646 422 0717; Email: vilar{at}cbio.mskcc.org

Received September 18, 2007. Revised October 29, 2007. Accepted October 29, 2007.

Transcription regulation typically involves the binding of proteins over long distances on multiple DNA sites that are brought close to each other by the formation of DNA loops. The inherent complexity of assembling regulatory complexes on looped DNA challenges the understanding of even the simplest genetic systems, including the prototypical lac operon. Here we implement a scalable approach based on thermodynamic molecular properties to model ab initio systems regulated through multiple DNA sites with looping. We show that this approach applied to the lac operon accurately predicts the system behavior for a wide range of cellular conditions, which include the transcription rate over five orders of magnitude as a function of the repressor concentration for wild type and all seven combinations of deletions of three operators, as well as the observed induction curves for cells with and without active catabolite activator protein. Our results provide new insights into the detailed functioning of the lac operon and reveal an efficient avenue to incorporate the required underlying molecular complexity into fully predictive models of gene regulation.


Present address: Leonor Saiz, Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.


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