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Bioinformatics Advance Access published online on June 25, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn330
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Computational design of synthetic gene circuits with composable parts

M.A. Marchisio 1,* and J. Stelling 1

1Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, 8092 Zurich, Switzerland

*To whom correspondence should be addressed. M.A. Marchisio, E-mail: mario.marchisio{at}inf.ethz.ch


  Abstract

Motivation: In principle, novel genetic circuits can be engineered using standard parts with well-understood functionalities. However, no model based on the simple composition of these parts has become a standard, mainly because it is difficult to define signal exchanges between biological units as unambiguously as in electrical engineering. Corresponding concepts and computational tools for easy circuit design in biology are missing.

Results: Taking inspiration from (and slightly modifying) ideas in the "MIT Registry of Standard Biological Parts", we developed a method for the design of genetic circuits with composable parts. Gene expression requires four kinds of signal carriers: RNA polymerases, ribosomes, transcription factors and environmental "messages" (inducers or corepressors). The flux of each of these types of molecules is a quantifiable biological signal exchanged between parts. Here, each part is modeled independently by the ordinary differential equations (ODE) formalism and integrated into the software ProMoT (Process Modeling Tool). In this way, we realized a "drag and drop" tool, where genetic circuits are built just by placing biological parts on a canvas and by connecting them through "wires" that enable flow of signal carriers, as it happens in electrical engineering. Our simulations of well-known synthetic circuits agree well with published computational and experimental results.

Availability: The code is available on request from the authors.

Contact: mario.marchisio{at}inf.ethz.ch

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Prof. Alfonso Valencia

Received on February 7, 2008; revised on May 28, 2008; accepted on June 23, 2008

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