Motifs Enable Communication Efficiency and Fault-Tolerance in Transcriptional Networks

Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets

Structure and topology of transcriptional regulatory networks and their applications in bio-inspired networking

Biological networks carry out vital functions necessary for sustenance despite environmental adversities. Transcriptional Regulatory Network (TRN) is one such biological network that is formed due to the interaction between proteins, called Transcription Factors (TFs), and segments of DNA, called genes. TRNs are known to exhibit functional robustness in the face of perturbation or mutation: a property that is proven to be a result of its underlying network topology. In this thesis, we first propose a three-tier topological characterization of TRN to analyze the interplay between the significant graph-theoretic properties of TRNs such as scale-free out-degree distribution, low graph density, small world property and the abundance of subgraphs called motifs. Specifically, we pinpoint the role of a certain three-node motif, called Feed Forward Loop (FFL) motif in topological robustness as well as information spread in TRNs. With the understanding of the TRN topology, we explore its potential use in design of fault-tolerant communication topologies. To this end, we first propose an edge rewiring mechanism that remedies the vulnerability of TRNs to the failure of well-connected nodes, called hubs, while preserving its other significant graph-theoretic properties. We apply the rewired TRN topologies in the design of wireless sensor networks that are less vulnerable to targeted node failure. Similarly, we apply the TRN topology to address the issues of robustness and energy-efficiency in the following networking paradigms: robust yet energy-efficient delay tolerant network for post disaster scenarios, energy-efficient data-collection framework for smart city applications and a data transfer framework deployed over a fog computing platform for collaborative sensing --Abstract, page iii

Role of Motifs in Topological Robustness of Gene Regulatory Networks

Gene Regulatory Networks (GRNs) are biological networks that have been widely studied for their ability to regulate protein synthesis in cells by robust signal propagation. The innate biological robustness of GRN is attributed to the occurrence of statistically significant subgraphs, called motifs. While Wireless Sensor Network (WSN) topologies designed using GRN graphs, called bio-WSNs, have been proven to exhibit significant improvement in packet delivery and network latency over random graph-based WSNs, it is still not clear what role motifs play in the observed performance improvement of bio-WSNs. This work explores why a dominant 3-node motif, called Feed Forward Loop (FFL), typifies the robustness of GRN motifs. We also employ graph centrality metrics to corroborate biological studies that have shown motifs to provide pathways for signal propagation in GRNs. Finally, we perform graph-theoretic and simulation experiments on GRN subgraphs and their corresponding bio-WSNs to demonstrate that nodes with high FFL motif participation offer multiple short and robust communication pathways, despite the failure of random and targeted nodes and links