TY - GEN

T1 - Neutral graph of regulatory Boolean networks using evolutionary computation

AU - Ruz, Gonzalo A.

AU - Goles, Eric

PY - 2014

Y1 - 2014

N2 - An evolution strategy is proposed to construct neutral graphs. The proposed method is applied to the construction of the neutral graph of Boolean regulatory networks that share the same state sequences of the cell cycle of the fission yeast. The regulatory networks in the neutral graph are analyzed, identifying characteristics of the networks which belong to the connected component of the fission yeast cell cycle network and the regulatory networks that are not in the connected component. Results show not only topological differences, but also differences in the state space between networks in the connected component and the rest of the networks in the neutral graph. It was found that regulatory networks in the fission yeast cell cycle network connected component can be mutated (change in their interaction matrices) no more than three times, if more mutations occur, then the networks leave the connected component. Comparisons with a standard genetic algorithm shows the effectiveness of the proposed evolution strategy.

AB - An evolution strategy is proposed to construct neutral graphs. The proposed method is applied to the construction of the neutral graph of Boolean regulatory networks that share the same state sequences of the cell cycle of the fission yeast. The regulatory networks in the neutral graph are analyzed, identifying characteristics of the networks which belong to the connected component of the fission yeast cell cycle network and the regulatory networks that are not in the connected component. Results show not only topological differences, but also differences in the state space between networks in the connected component and the rest of the networks in the neutral graph. It was found that regulatory networks in the fission yeast cell cycle network connected component can be mutated (change in their interaction matrices) no more than three times, if more mutations occur, then the networks leave the connected component. Comparisons with a standard genetic algorithm shows the effectiveness of the proposed evolution strategy.

UR - http://www.scopus.com/inward/record.url?scp=84904438389&partnerID=8YFLogxK

U2 - 10.1109/CIBCB.2014.6845529

DO - 10.1109/CIBCB.2014.6845529

M3 - Conference contribution

AN - SCOPUS:84904438389

SN - 9781479945368

T3 - 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014

BT - 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014

PB - IEEE Computer Society

T2 - 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014

Y2 - 21 May 2014 through 24 May 2014

ER -