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Biophys. J. BioFAST: First Published August 18, 2006. doi:10.1529/biophysj.106.083485
© 2006 by the Biophysical Society.

A more recent version of this article appeared on November 1, 2006.
Originally published as Biophys J. BioFAST on August 18, 2006.
doi:10.1529/biophysj.106.083485
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BIOPHYSICAL THEORY AND MODELING

Optimization of a Stochastically-Integrated Gene Network Model via Simulated Annealing

Jonathan Tomshine 1 and Yiannis Kaznessis 1*

1 University of Minnesota

* To whom correspondence should be addressed. E-mail: yiannis{at}cems.umn.edu.

Submitted on February 15, 2006
Revised on April 9, 2006
Accepted on 2 August 2006


  Abstract
By rearranging naturally occurring genetic components, gene networks can be created that display novel functions. When designing these networks, the kinetic parameters describing DNA/protein binding are of great importance, as these parameters strongly influence the behavior of the resulting gene network. This paper presents an optimization method based on simulated annealing to locate combinations of kinetic parameters that produce a desired behavior in a genetic network. Since gene expression is an inherently stochastic process, the simulation component of SA optimization is conducted using an accurate multiscale simulation algorithm to calculate an ensemble of network trajectories at each iteration of the SA algorithm. Using the three-gene repressilator of Elowitz and Leibler as an example, we show that gene network optimizations can be conducted using a mechanistically realistic model integrated stochastically. The repressilator is optimized to give oscillations of an arbitrary specified period. These optimized designs may then provide a starting-point for the selection of genetic components needed to realize an in vivo system.

Key Words: Gene Network Engineering, Gene Regulation, Multiscale Stochastic Simulation, Reaction Kinetics Optimization




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Copyright © 2006 by the Biophysical Society.