Bioinformatics Advance Access originally published online on July 27, 2007
Bioinformatics 2007 23(18):2415-2422; doi:10.1093/bioinformatics/btm362
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Robustness analysis and tuning of synthetic gene networks
1Centers for Information and Systems Engineering and for BioDynamics, 2Department of Biomedical Engineering, Boston University, Boston, MA and 3Departments of Electrical Engineering and Molecular Biology, Princeton University, Princeton, NJ, USA
*To whom correspondence should be addressed.
| Abstract |
|---|
Motivation: The goal of synthetic biology is to design and construct biological systems that present a desired behavior. The construction of synthetic gene networks implementing simple functions has demonstrated the feasibility of this approach. However, the design of these networks is difficult, notably because existing techniques and tools are not adapted to deal with uncertainties on molecular concentrations and parameter values.
Results: We propose an approach for the analysis of a class of uncertain piecewise-multiaffine differential equation models. This modeling framework is well adapted to the experimental data currently available. Moreover, these models present interesting mathematical properties that allow the development of efficient algorithms for solving robustness analyses and tuning problems. These algorithms are implemented in the tool RoVerGeNe, and their practical applicability and biological relevance are demonstrated on the analysis of the tuning of a synthetic transcriptional cascade built in Escherichia coli.
Availability: RoVerGeNe and the transcriptional cascade model are available at http://iasi.bu.edu/%7Ebatt/rovergene/rovergene.htm
Contact: gregory.batt{at}imag.fr
Associate Editor: Chris Stoeckert
Received on March 17, 2007; revised on June 8, 2007; accepted on July 8, 2007