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PLoS One

Title:		Optimal in silico target gene deletion through nonlinear programming for genetic engineering
Author(s):		Hong CC; Song M;
Address:		"Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, United States of America"
Journal Title:		PLoS One
Year:		2010
Volume:		20100224
Issue:		2
Page Number:		e9331 -
DOI:		10.1371/journal.pone.0009331
ISSN/ISBN:		1932-6203 (Electronic) 1932-6203 (Linking)
Abstract:		"BACKGROUND: Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized. METHODOLOGY/PRINCIPAL FINDINGS: Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy. SIGNIFICANCE: Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure"
Keywords:		"*Algorithms Computational Biology/*methods *Gene Deletion Gene Regulatory Networks Genetic Engineering/*methods Metabolic Networks and Pathways Models, Genetic Pheromones/metabolism Proteins/genetics/metabolism Reproducibility of Results Saccharomyces cer;"
Notes:		"MedlineHong, Chung-Chien Song, Mingzhou eng U54 CA132383/CA/NCI NIH HHS/ U54 CA132383-03/CA/NCI NIH HHS/ 5U54CA132383/CA/NCI NIH HHS/ Research Support, N.I.H., Extramural 2010/03/03 PLoS One. 2010 Feb 24; 5(2):e9331. doi: 10.1371/journal.pone.0009331"