Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016:2016:1060843.
doi: 10.1155/2016/1060843. Epub 2016 May 19.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

Abhinandan Khan  1 , Sudip Mandal  1 , Rajat Kumar Pal  1 , Goutam Saha  2
Affiliations

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

Abhinandan Khan et al. Scientifica (Cairo). 2016.

Abstract

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Gene regulation with both positive and negative feedback.
Figure 2
Figure 2
RNN model description of a genetic network. The network shown is unfolded from t = t 1 to t = t 4. Here, all possible connections have been shown among the genes whereas, in reality, such networks are only sparsely connected.
Figure 3
Figure 3
Network dynamics used for training the proposed model. The four lines represent the expression profile of the four genes.
Figure 4
Figure 4
True positive (TP) and false positive (FP) counts obtained by the proposed BAPSO model, compared with those obtained by Xu et al. [34] and Kentzoglanakis and Poole [46] and PSO. The results of the BAPSO model have been presented for two datasets: one with 50 time points, represented as BAPSO_full, and the other with 25 time points, represented as BAPSO_half.
Figure 5
Figure 5
The original structure of the SOS DNA repair transcriptional network of E. coli.
Figure 6
Figure 6
Network architecture extracted from GNW to validate the proposed framework as used in [46].
Figure 7
Figure 7
Inferred network obtained by the proposed model for 50 time points.
Figure 8
Figure 8
Inferred network obtained by the proposed model for 25 time points.
Figure 9
Figure 9
Original 20-gene network extracted from the genome of E. coli stored in GNW.
See this image and copyright information in PMC

References

    1. McLachlan G. J., Do K.-A., Ambroise C. Analyzing Microarray Gene Expression Data. Vol. 422. New York, NY, USA: John Wiley & Sons; 2005. - DOI
    1. Xu R., Wunsch D., II Survey of clustering algorithms. IEEE Transactions on Neural Networks. 2005;16(3):645–678. doi: 10.1109/TNN.2005.845141. - DOI - PubMed
    1. Bar-Joseph Z. Analyzing time series gene expression data. Bioinformatics. 2004;20(16):2493–2503. doi: 10.1093/bioinformatics/bth283. - DOI - PubMed
    1. D'haeseleer P., Wen X., Fuhrman S., Somogyi R. Linear modelling of mRNA expression levels during CNS development and injury. Pacific Symposium on Biocomputing; 1999; pp. 41–52. - PubMed
    1. De Jong H., Gouzé J.-L., Hernandez C., Page M., Sari T., Geiselmann J. Qualitative simulation of genetic regulatory networks using piecewise-linear models. The Bulletin of Mathematical Biology. 2004;66(2):301–340. doi: 10.1016/j.bulm.2003.08.010. - DOI - PubMed