Hesselson D et al. (2006) J Cell Biol "Growth control by EGF repeats of the C. elegans Fibulin-1C isoform."

Hesselson D, Kimble J

[J Cell Biol, 2006]

Fibulin is a broadly conserved component of the extracellular matrix (ECM). Previous studies have shown that Caenorhabditis elegans FIBULIN-1 (FBL-1) controls the width of the gonad (Hesselson, D., C. Newman, K.W. Kim, and J. Kimble. 2004. Curr. Biol. 14:2005-2010; Kubota, Y., R. Kuroki, and K. Nishiwaki. 2004. Curr. Biol. 14:2011-2018; Muriel, J.M., C. Dong, H. Hutter, and B.E. Vogel. 2005. Development. 132: 4223-4234). In this study, we report that FBL-1 also controls developmental growth and that one isoform of fibulin-1, called FBL-1C, controls both functions by distinct mechanisms. A large FBL-1C fragment, including both epidermal growth factor (EGF) and fibulin-type C domains, is responsible for constraining gonadal width, but a much smaller fragment containing only two complete EGF repeats (EGF1-2C+) is critical for developmental growth. We suggest that the larger fragment serves a scaffolding function to stabilize the basement membrane and that the smaller fragment provides a regulatory function at the cell surface or within the ECM to control growth.

Narayanan T et al. (2014) IEEE Trans Biomed Circuits Syst "A Newtonian framework for community detection in undirected biological networks."

Subramaniam S, Narayanan T

[IEEE Trans Biomed Circuits Syst, 2014]

Community detection is a key problem of interest in network analysis, with applications in a variety of domains such as biological networks, social network modeling, and communication pattern analysis. In this paper, we present a novel framework for community detection that is motivated by a physical system analogy. We model a network as a system of point masses, and drive the process of community detection, by leveraging the Newtonian interactions between the point masses. Our framework is designed to be generic and extensible relative to the model parameters that are most suited for the problem domain. We illustrate the applicability of our approach by applying the Newtonian Community Detection algorithm on protein-protein interaction networks of E. coli , C. elegans, and S. cerevisiae. We obtain results that are comparable in quality to those obtained from the Newman-Girvan algorithm, a widely employed divisive algorithm for community detection. We also present a detailed analysis of the structural properties of the communities produced by our proposed algorithm, together with a biological interpretation using E. coli protein network as a case study. A functional enrichment heat map is constructed with the Gene Ontology functional mapping, in addition to a pathway analysis for each community. The analysis illustrates that the proposed algorithm elicits communities that are not only meaningful from a topological standpoint, but also possess biological relevance. We believe that our algorithm has the potential to serve as a key computational tool for driving therapeutic applications involving targeted drug development for personalized care delivery.

Narayanan T et al. (2011) BMC Res Notes "Modularity detection in protein-protein interaction networks."

Subramaniam S, Gersten M, Narayanan T, Grama A

[BMC Res Notes, 2011]

UNLABELLED: ABSTRACT: BACKGROUND: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. RESULTS: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C. Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. CONCLUSIONS: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to efficiently guide the modularity detection process. We have empirically evaluated our approach in the specific context of real-world large scale biological networks, and have demonstrated significant savings in computational time while maintaining comparable quality of detected modules.