Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm.
BACKGROUND
To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation.


RESULTS
We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.
Kimura
Shuhei
S
Bioinformatics Group, RIKEN Genomic Sciences Center 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan. skimura@gsc.riken.jp
Ide
Kaori
K
Kashihara
Aiko
A
Kano
Makoto
M
Hatakeyama
Mariko
M
Masui
Ryoji
R
Nakagawa
Noriko
N
Yokoyama
Shigeyuki
S
Kuramitsu
Seiki
S
Konagaya
Akihiko
A
D000465:Algorithms; D001185:Artificial Intelligence; D019143:Evolution, Molecular; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D014157:Transcription Factors / Q000502:physiology*
D014157:Transcription Factors