Qualitative Motif Detection in Gene Regulatory Networks

Abstract

This paper motivates the use of Qualitative Probabilistic Networks (QPNs) in conjunction with or in lieu of Bayesian Networks (BNs) for reconstructing gene regulatory networks from microarray expression data. QPNs are qualitative abstractions of Bayesian Networks that replace the conditional probability tables associated with BNs by qualitative influences, which use signs to encode how the values of variables change. We demonstrate that the qualitative influences defined by QPNs exhibit a natural mapping to naturally-occurring patterns of connections, termed network motifs, embedded in Gene Regulatory Networks and present a model that maps QPN constructs to such motifs.The contribution of this paper is that of discovering motifs by mapping their time-series experimental data to QPN influences and using the discovered motifs to aid the process of reconstructing the corresponding gene regulatory network via Dynamic Bayesian Networks (DBNs). The general aim is to compile a model that uses qualitative equivalents of Dynamic Bayesian Networks to explore gene expression networks and their regulatory mechanisms. Although this aim remains under development, the results we have obtained shows success for the discovery of regulatory motifs in Saccharomyces Cerevisiae and their effectiveness in improving the results obtained in terms of reconstruction using DBNs.

A dynamic qualitative probabilistic network approach for extracting gene regulatory network motifs

“…In [16], we demonstrated that the qualitative influences defined by DQPNs exhibit a natural mapping to some of the naturally-occurring network motifs of gene regulatory networks. However, DQPN influences are only capable of representing network motifs where one regulator gene exists for each regulated gene.…”

“…These issues were addressed by the model we developed in [16], [17]. In essence, the idea was to incorporate background knowledge in the form of high-level common-sense information extracted from the data and utilize it to aid the Bayesian algorithm learning the genetic network structure; therefore reducing the search space to only include the models that agree with the background knowledge.…”

“…For this, we created Dynamic Qualitative Probabilistic Networks (DQPNs) [16], [17], qualitative abstractions of Dynamic Bayesian Networks, which are commonly used in gene regulatory network reconstruction [22]. DQPNs replace the conditional probability tables associated with DBNs by qualitative influences, which use signs to encode how the values of probabilities change instead of their exact values.…”

“…Having showed in [16] that the qualitative relations defining QPN graphs exhibit a direct mapping to the naturally-occurring network motifs embedded in Gene Regulatory Networks, this work is concerned with generalizing QPN constructs to create a high-level framework from which any regulatory network motif can be derived.Experimental results using time-series data of the Saccharomyces Cerevisiae show the effectiveness of our approach in providing a more accurate description of the regulatory motifs in the Saccharomyces Cerevisiae gene regulatory network compared to our previous definitions. …”

“…Having showed in [16] that the qualitative relations defining QPN graphs exhibit a direct mapping to the naturally-occurring network motifs embedded in Gene Regulatory Networks, this work is concerned with generalizing QPN constructs to create a high-level framework from which any regulatory network motif can be derived.…”