CORE-Net: Convex Optimization algorithm for Reverse–Engineering biological interaction Networks

CORE-Net is an algorithm for reverse engineering biological interaction networks from both steady-state and time-course experimental data, based on dynamical systems and convex optimization. It was developed by Francesco Montefusco and Carlo Cosentino in the Biomechatronics Laboratory, directed by Prof. Francesco Amato, at the Magna Græcia University of Catanzaro, Italy.

The distinctive feature of this method is that it enables to straightforwardly exploit qualitative prior knowledge available from the biological domain, thus significantly increasing the inference performance.

CORE-Net is specifically suited to infer gene regulatory networks exhibiting scale-free topology: the iterative reconstruction algorithm, indeed, is based on growth and preferential attachment, which are the same basic mechanisms underlying the model of scale-free networks proposed by Albert and Barabási. Moreover, many biological networks have been demonstrated to exhibit this kind of topology.

In order to cope also with non-scale-free networks, an alternative version, named CORE-Net_noPA is also available: it is based on the same convex optimization algorithm, however it does not implement the preferential attachment rule, thus resulting better suited for inferring networks with random Erdős–Rényi topology.

All the softwares provided on this page are free for non commercial use and the source code can be downloaded here.

Documentation

The software documentation is included in the source code packages in the download section.

Relevant Publications

The CORE-Net algorithm is described in the following companion paper

[1] F. Montefusco, C. Cosentino, F. Amato, CORE–Net: Exploiting Prior Knowledge and Preferential Attachment to Infer Biological Interaction Networks, submitted to IET Syst. Biol. (Aug. 2009)

Other publications by our group regarding convex optimization and regression methods to infer biological networks

• F. Amato, C. Cosentino, F. Montefusco, Inferring Gene Regulatory Networks with a Partially Known Scale-Free Topology, European Control Conference 2009 (ECC’09), Budapest, Hungary, August 23-26, 2009.

• F. Amato, C. Cosentino, F. Montefusco, Exploiting Prior Knowledge and Preferential Attachment to Infer Biological Interaction Networks, Proc. of the 17th Mediterranean Conference on Control and Automation 2009 (MED’09), Thessaloniki, Greece, June 24-26, 2009.

• F. Amato, C. Cosentino, F. Montefusco, Inferring Scale-Free Networks via Multiple Linear Regression and Preferential Attachment, Proc. of the 16th Mediterranean Conference on Control and Automation 2008 (MED’08), Ajaccio, Corsica, June 25-27, 2008.

• C. Cosentino, W. Curatola, F. Montefusco, M. Bansal, D. di Bernardo, F. Amato, Linear Matrix Inequalities Approach to Reconstruction of Biological Networks, IET Syst. Biol., Vol. 1, no. 3, pp. 164–173, May 2007.

• C. Cosentino, W. Curatola, M. Bansal, D. di Bernardo, and F. Amato, Piecewise Affine Approach to Inferring Cell Cycle Regulatory Network in Fission Yeast, Biomedical Signal Processing and Control Vol. 2, pp. 208–216, 2007.

Application Examples

CORE-Net has been statistically validated by testing it over a large number of in silico networks, using both random Erdős–Rényi and scale—free topologies. The numerical dataset and the code for generating and simulating the in silico networks are available in the dowload section.

A biological case-study is presented in [1], dealing with the reconstruction of a cell cycle gene regulatory subnetwork in S. cerevisiae. The dataset and the gold-standard network to be inferred are available in the download section. 

The results of CORE-Net and CORE-Net_noPA have been compared with those obtained by two well assessed reverse engineering algorithm, which are freely available from the authors' websites:

• BANJO (BAyesian Network inference with Java Objects), which is based on the theory of Bayesian networks;

• CLR (Context Likelihood of Relatedness), which is based on Mutual Information theory.

More information about these tests are reported in [1].

Requirements

CORE-Net has been developed in Matlab^® R2007b and needs some routines included in the Control System, Robust Control and Spline Toolboxes.

Download

Important: This software is distributed only for non-commercial purposes and only for academic use - Commercial users please contact us.

• Supplementary material for [1]:

  • Source code to perform the reverse engineering tests reported in [1], with CORE-Net, CORE-Net_noPA, BANJO and CLR on both the in silico and the in vitro data sets. In order to run the tests with BANJO and CLR, it is required to download and install the software from the respective authors' web sites.

Single packages download

• CORE-Net ver. 1.0 package including Matlab^® source code;

• CORE-Net_noPA ver. 1.0 package including Matlab^® source code;

• Source code to generate in-silico networks with random scale–free or Erdős–Rényi topology;

• The in silico data set used in [1] can be downloaded in  two formats:

  • Matlab^® files (for use with CORE-Net, CORE-Net_noPA, CLR);

  • Plain text files (for use with BANJO).

• Data set for the biological case study tackled in [1]:

  • Matlab^® files (for use with CORE-Net, CORE-Net_noPA, CLR);

  • Plain text files (for use with BANJO).

Contacts

If you have questions/comments/suggestions, please do not hesitate to contact us.

Dr. Carlo Cosentino

Assistant Professor of Systems and Control Theory

Department of Experimental and Clinical Medicine

Magna Graecia University of Catanzaro

v.le Europa, Campus Salvatore Venuta

88100 Catanzaro

Italy

Tel: +39-0961-369-4051

Fax: +39-0961-369-4090

e-mail: carlo.cosentino at unicz.it