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

This paper extends our work to using qualitative probability to model the naturally-occurring motifs of gene regulatory networks. 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.