Computational and biological inference of gene regulatory networks of the LINE-1 retrotransposon

Computational approaches were used to define structural and functional determinants of a putative genetic regulatory network of murine LINE-1 (long interspersed nuclear element-1), an active mammalian retrotransposon that uses RNA intermediates to populate new sites throughout the genome. Polymerase (RNA) II polypeptide E AI845735 and mouse DNA homologous to Drosophila per fragment M12039 were identified as primary attractors. siRNA knockdown of the aryl hydrocarbon receptor NM_013464 modulated gene expression within the network, including LINE-1, Sgpl1, Sdcbp, and Mgst1. Genes within the network did not exhibit physical proximity and instead were dispersed throughout the genome. The potential impact of individual members of the network on the global dynamical behavior of LINE-1 was examined from a theoretical and empirical framework.     

Computational approaches for modeling regulatory cellular networks

Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.     

Computational discovery of regulatory elements in a continuous expression space

Approaches for regulatory element discovery from gene expression data usually rely on clustering algorithms to partition the data into clusters of co-expressed genes. Gene regulatory sequences are then mined to find overrepresented motifs in each cluster. However, this ad hoc partition rarely fits the biological reality. We propose a novel method called RED² that avoids data clustering by estimating motif densities locally around each gene. We show that RED² detects numerous motifs not detected by clustering-based approaches, and that most of these correspond to characterized motifs. RED² can be accessed online through a user-friendly interface.