SiGN-SSM is open source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by the statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles.
SiGN-SSM is distributed under GNU AFFERO GENERAL PUBLIC LICENCE (GNU AGPL) version 3. The pre-compile binaries for Linux (x86-64), MS Windows, and Mac OS X are also available in addition to the source code. The pre-installed binaries are available on the Human Genome Center (HGC) supercomputer system  and the Japanese flagship supercomputer "K computer" .
The main paper describing SiGN-SSM is Tamada et al. (2011)  in REFERENCE below. Please cite this if you want to refer to SiGN-SSM in your publication, etc. The mathematical details are described in Hirose et al. (2008)  and Yamaguchi et al. (2008) .
The example of the application to the real data analysis is Yamauchi et al. (2012) .
SiGN-SSM is developed in the ISLiM (Next-generation integrated simulation of living matter) project in RIKEN Computational Science Research Program . This is based on the previous implementation TRANS-MNET . Computational resources required for the development of SiGN-SSM was provided by the HGC Supercomputer System, Human Genome Center, Institute of Medical Science, The University of Tokyo; and RIKEN Supercomputer system RICC.
 Tamada, Y., Yamaguchi, R., Imoto, S., Hirose, O., Yoshida, R., Nagasaki, M., and Miyano, S. (2011). SiGN-SSM: open source parallel software for estimating gene networks with state space models. Bioinformatics 27 (8), 1172-1173.
 Hirose, O., Yoshida, R., Imoto, S., Yamaguchi, R., Higuchi, T., Charnock-Jones, D.S., Print, C., and Miyano, S. (2008). Statistical inference of transcriptional module-based gene enetworks from time course gene expression profiles by using state space models. Bioinformatics 24 (7), 932-942.
 Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Hatanaka, Y., Ueno, K., Higuchi T., Gotoh, N., and Miyano, S. (2008). Predicting differences in gene regulatory systems by state space models. Genome Informatics 21, 101-113.
 Wu, L.S.-Y., Pai, J.S, and Hosking J.R.M. (1996). An algorithm for estimating parameters of state-space models. Statistics & Probability Letters 28, 99-106.
 LINK: HGC Supercomputer System
 LINK: RIKEN AICS "K computer"
 Yamauchi, M., Yamaguchi, R., Nakata, A., Kohno, T., Nagasaki, M., Shimamura, T., Imoto, S., Saito, A., Ueno, K., Hatanaka, Y., Yoshida, R., Higuchi, T., Nomura, M., Beer, D.G., Yokota, J., Miyano, S., and Gotoh, N. (2012). Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma. PLoS ONE 7(9), e43923. (See at PLoS ONE)