In-silico Models for Capturing the Static and Dynamic Characteristics of Robustness within Complex Networks

Understanding the role of structural patterns within complex networks is essential to establish the governing principles of such networks. Social networks, biological networks, technological networks etc. can be considered as complex networks where information processing and transport plays a central role. Complexity in these net works can be due to abstraction, scale, functionality and structure. Depending on the abstraction each of these can be categorized further. Gene regulatory networks are one such category of biological networks. Gene regulatory networks (GRNs) are assumed to be robust under internal and external perturbations. Network motifs such as feed-forward loop motif and bifan motif are believed to play a central role functionally in retaining GRN behavior under lossy conditions. While the role of static characteristics like average shortest path, density, degree centrality among other topological features is well documented by the research community, the structural role of motifs and their dynamic characteristics are not xiii well understood. Wireless sensor networks in the last decade were intensively studied using network simulators. Can we use in-silico experiments to understand biological network topologies better? Does the structure of these motifs have any role to play in ensuring robust information transport in such networks? How do their static and dynamic roles differ? To understand these questions, we use in-silico network models to capture the dynamic characteristics of complex network topologies. Developing these models involve network mapping, sink selection strategies and identifying metrics to capture robust system behavior. Further, I studied the dynamic aspect of network characteristics using variation in network information flow under perturbations defined by lossy conditions and channel capacity. We use machine learning techniques to identify significant features that contribute to robust network performance. Our work demonstrates that although the structural role of feed-forward loop motif in signal transduction within GRNs is minimal, these motifs stand out under heavy perturbations

DISMIRA: 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 miRNA regulations in a disease 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

Quantifying Robustness in Biological Networks using NS-2

Biological networks are known to be robust despite signal disruptions such as gene failures and perturbations. Extensive research is currently under way to explore biological networks and identify the underlying principles of their robustness. Structural properties such as power-law degree distribution and motif abundance have been attributed for robust performance of biological networks. Yet, little has been done so far to quantify such biological robustness. We propose a platform to quantify biological robustness using network simulator (NS-2) by careful mapping of biological properties at the gene level to that of wireless sensor networks derived using the topology of gene regulatory networks found in different organisms. A Support Vector Machine (SVM) learning model is used to measure the correlation of packet transmission rates in such sensor networks. These sensor networks contain important topological features of the underlying biological network, such as motif abundance, node/gene coverage, and transcription-factor network density, which we use to map the SVM features. Finally, a case study is presented to evaluate the NS-2 performance of two gene regulatory networks, obtained from the bacterium Escherichia coli and the baker\u27s yeast Sachharomyces cerevisiae