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Open Access Highly Accessed Research article

An algebra-based method for inferring gene regulatory networks

Paola Vera-Licona12*, Abdul Jarrah3, Luis David Garcia-Puente4, John McGee5 and Reinhard Laubenbacher126

Author Affiliations

1 Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT 06030-6029, USA

2 Department of Cell Biology, University of Connecticut Health Center, Farmington, CT 06030, USA

3 Department of Mathematics and Statistics, American University of Sharjah, Sharjah, UAE

4 Department of Mathematics and Statistics, Sam Houston State University, Huntsville, TX 77341-2206, USA

5 Mathematics and Statistics Department, Radford University, Radford, VA 24142, USA

6 Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA

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BMC Systems Biology 2014, 8:37  doi:10.1186/1752-0509-8-37

Published: 26 March 2014

Abstract

Background

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.

Results

This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.

Conclusions

Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html webcite.

Keywords:
Reverse-engineering; network inference; Boolean networks; molecular networks; gene regulatory networks; polynomial dynamical systems; algebraic dynamic models; evolutionary computation; DNA microarray data; time series data; data noise