Gene regulatory network modeling: A data driven approach

Abstract

In this chapter, a novel gene regulatory network gene regulatory network inference algorithm based on the fuzzy logic network fuzzy logic network is proposed and tested. The algorithm is intuitive and robust. The key motivation for this algorithm is that genes with regulatory relationships can be modeled via fuzzy logic, and the degrees of regulations can be represented as the accumulated distance during a period of time intervals. One unique feature of this algorithm is that it makes very limited prior assumptions concerning the modeling; hence the algorithm is categorized as a data-driven algorithm data-driven algorithm. As a non-parametric model, the algorithm is very useful when only limited a priori knowledge on the target microarray exists. Another characteristic of this algorithm is that the time-series microarray data have been treated as a dynamic gene regulatory network gene regulatory network, and the fuzzification of gene expression values makes the algorithm more agreeable to reality. We have deduced the dynamic properties of the FLN using the anneal approximation, and dynamic equations of the FLN have been analyzed. Based upon previous investigation results that in yeast protein-protein networks, as well as in the Internet and social networks, the distribution of connectivity follows the Zipf's law, the criteria of parameter quantifications for the algorithm have been achieved. The algorithm was applied on the yeast cell-cycle dataset from Stanford Saccharomyces cerevisiae database which produced pleasing results. The computation also showed that a triplet search is suitable and efficient for the inference of gene regulatory networks. The chapter is organized as follows: first, the broader context of gene regulatory networks research is addressed. Then the definition of the FLN is given, and the dynamic properties of the FLN are deduced using the annealed approximation annealed approximation in Sect. FLN Theory. Combined with the Zipf's law, the parameter quantification guidelines are achieved at the end of this section. In Sect. Algorithm Design, the algorithm is illustrated in detail with its data-driven rationale. Then, the algorithm's inference results on Saccharomyces cerevisiae dataset are presented and analyzed in Sect. Computation Results. Future research work with the algorithm is discussed at the end of this chapter. © 2007 Springer-Verlag Berlin Heidelberg.