Optimization of a stochastically simulated gene network model via simulated annealing

Jonathan Tomshine, Yiannis N. Kaznessis

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

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 article 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 simulated annealing optimization is conducted using an accurate multiscale simulation algorithm to calculate an ensemble of network trajectories at each iteration of the simulated annealing 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.

Original languageEnglish (US)
Pages (from-to)3196-3205
Number of pages10
JournalBiophysical journal
Volume91
Issue number9
DOIs
StatePublished - Nov 2006

Bibliographical note

Funding Information:
Computational support from the Minnesota Supercomputing Institute is gratefully acknowledged.

Funding Information:
This work was supported by National Science Foundation grant No. BES-0425882. This work was also supported by the National Computational Science Alliance under grant No. TG-MCA04N033.

Fingerprint Dive into the research topics of 'Optimization of a stochastically simulated gene network model via simulated annealing'. Together they form a unique fingerprint.

Cite this