Analysing the Structure of Biological and other Networks

Deyasi, Krishanu (2017) Analysing the Structure of Biological and other Networks. PhD thesis, Indian Institute of Science Education and Research Kolkata.

PDF (PhD thesis of Krishanu Deyasi (11RS002)) Thesis_Krishanu_Deyasi.pdf - Submitted Version Restricted to Repository staff only Download (2MB)

Abstract

Network is widely used to represent interaction pattern among the components in a complex system. Structures of real networks from different domains may vary quite significantly. Since there is an interplay between network architecture and dynamics, structure plays an important role in communication and information spreading on a network. We investigate the underlying undirected topologies of different biological networks which support faster spreading of information and are better in communication. We analyse the good expansion property by using the spectral gap and communicability between nodes of a network. Different epidemic models are also used to study the transmission of information in terms of disease spreading through individuals in those networks. We explore the structural conformation and properties which may be responsible for better communication. Among all biological networks studied, the undirected structure of neuronal networks not only possess the small-world property but also the same is expressed remarkably to a higher degree than any randomly generated network which possesses the same degree sequence. A relatively high percentage of nodes, in neuronal networks, form a higher core in their structure. Our study shows that the underlying undirected topology in neuronal networks is significantly qualitatively different than the same from other biological networks and they may have evolved in such a way that they inherit a structure which is excellent and robust in communication. Many methods have been developed for finding the commonalities between different organisms to study their phylogeny. The structure of metabolic networks also reveal valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We construct metabolic networks of 79 fully sequenced organisms and compare their architectures. We use spectral density of normalised Laplacian matrix for comparing structure of the networks. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the evolutionary relationship from these distances. In our analysis, we show more interest on the species, who belong to different classes, but come in the vicinity of each other in our clustering. We try to explore whether they have evolved in similar environmental condition or lifestyle, and we reveal interesting insights into the phylogenetic commonality between different organisms. On the other hand, in non-biological networks, we study the structural similarity of earthquake networks constructed from seismic catalogue of different geographical regions. A hierarchical clustering of underlying undirected earthquake networks is made from network distances. The directed nature of links indicates that each earthquake network is strongly connected, which motivates us to study the directed version statistically. Our statistical analysis of each earthquake region identifies the hubs. To predict the consecutive earthquake events, we calculate the conditional probability of the forthcoming occurrences of earthquakes in each region. The conditional probability of each event has also been compared with their stationary distribution. Study of the dynamics on a network can reveal unknown connections within the nodes or discover new nodes of that network. We investigate the same in regard to gene regulatory networks. This study can be helpful for experimentalists, as the prediction of unrevealed connections or nodes allows designing of experiments based on specific hypothesis. Here dynamical modelling is used along with parallel lab experiments. Our results allow the identification of a new molecule (node) which is responsible for observed experimental dynamics.

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