Sparse and Stable Reconstruction of Genetic Regulatory Networks Using Time Series Gene Expression Data

Authors:
Roozbeh Manshaei;Matthew Kyan
Affiliations:
Ryerson University, ECE Department, Eric Palin Hall, 87 Gerrard Street East, Toronto, Canada;Ryerson University, ECE Department, Eric Palin Hall, 87 Gerrard Street East, Toronto, Canada
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

Citing 2
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Convex Optimization

Convex Optimization
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

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Abstract

Gene regulatory networks represent the regulatory and physical interactions between genes of an organism. In this application, we are presented with a set of time series gene expression data, from which an unknown topology describing the regulatory interactions between genes must be inferred. To this end, we formulate an algorithm for reconstructing a genetic regulatory network to explain time series data obtained from genetic experiments. Our algorithm minimizes the trade-off between of the sparsity of gene interactions in the inferred network and the best model accuracy, where stability and prior knowledge are considered as constraints. Our algorithm is applied to time series gene expression data from yeast cell-cycle regulation, and results show improved reconstruction. The convex nature of the proposed model makes it suitable for application to large-scale networks.