Dynamical impacts from structural redundancy of transcriptional motifs in gene-regulatory networks

Biological networks are now studied extensively by researchers to understand the working principles of nature. Machine learning techniques can be useful in realizing the features contributing to the robustness of biological systems. In this work, we compare subnetworks extracted from E. coli and yeast using in silico experiments. We use packet receival rate as a metric to quantify biological robustness. This metric is different from the usual structural metrics since it captures the dynamic behavior of the network. We define seventeen features based on structural significance such as motifs and conventional metrics such as average shortest path, network density among others. Then, in order to identify important features, feature ranking is performed based on grid search for best support vector machine classifier parameters using cross validation. Results show that feed-forward loop motif based features are important for E. coli networks. Network density, degree centrality based features and bifan motif based features are identified as significant for yeast derived networks. Also, results suggest that feature significance varies with network size (number of nodes). As a first, this study quantifies the impact of motif, feed-forward loop and bifan, abundance in natural networks.

Dynamical impacts from structural redundancy of transcriptional motifs in gene-regulatory networks

We examine and compare transcriptional networks extracted from the bacterium Escherichia coli and the baker's yeast Saccharomyces cerevisiae using discrete event simulation based in silico experiments. The packet receipt rate is used as a dynamical metric to understand information ow, while machine learning techniques are used to examine underlying relationships inherent to the network topology. To this effect, we defined sixteen features based on structural/topological significance, such as transcriptional motifs,and other traditional metrics, such as network density and average shortest path, among others. Support vector classification is carried out using these features after parameters were identified using a cross-validation grid-search method. Feature ranking is performed using analysis of variance F-value metric. We found that feed-forward loop based features rank consistently high in both the bacterial and yeast networks, even at different perturbation levels. This work paves the way to design specialized engineered systems, such as wireless sensor networks, that exploit topological properties of natural networks to attain maximum efficiency.

Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis

Background: MicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNAs regulating a disease and vice-versa are still elusive.

Methods: In this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.

Results and Conclusion: Our tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.