Analysis of genomic and proteomic networks

Abstract

Evolution is driven by biological variation at many levels viz., genome, proteome, interactome. Mutations and rearrangements in genomic DNA as well as variations at the protein level, lead to changes in protein structures, abundances, and modification states. These molecular changes further impact how proteins interact with one another, with DNA, and with small molecules to form signalling, regulatory, and metabolic networks [1]. Ultimately, network organization has sweeping implications on changes in cellular function, tissue-level responses, and the behaviour and morphology of whole organisms [1, 2]. Molecular networks are ubiquitous and persist several levels of complexity, configuration, hierarchy and function [2]. Thus, studying variation in biological systems under the context of networks is essential and promising. While genomics and proteomics with transcriptomics are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge [3]. As traditional biology is moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful both evolutionary undertanding and precision medicine. Here, network biology becomes a key paradigm for applying omics data to develop models of cellular function. Biological network modelling is characterized by viewing cells in terms of their underlying network structure at many different levels of detail is a cornerstone of systems biology [2]. The rapid growth of sequence data and molecular network information raises a host of new questions in evolutionary and comparative biology. However, how to develop novel strategies and theoretical frameworks to filter, organize and interpret molecular interaction networks? is still a major challenge. Nonetheless, a number of recent advances have made it possible to begin to define this field in terms of the computational methodology it requires and the biological questions it may be able to answer [4]. Therefore, in addition to the difficulties, the emerging interaction networks simultaneously provide an exciting opportunity for understanding biology. For example, analyzing the disease networks can lead to a better understanding of disease mechanisms by identifying the disease-causing genes and pathways to offer targets for drug discovery. The dominant objective of network science has been on developing universal theories and models that transcend system-specific details and describe the different systems in a meaningful yet statistical sense. Biological networks appear in extremely diverse contexts. Still, many of these networks share certain nontrivial, similar patterns in connections between their elements. Understanding the origins of these patterns and identifying and characterizing new ones is one of the main driving forces for research in complex biological networks. Complex biological networks have two essential elements: nodes and edges. Nodes represent units in the network (e.g., genes or mRNA, protein), while edges represent the interactions between the units (e.g, protein-protein binding). The organizational features of interaction graphs can be quantified by network measures whose information content ranges from local (e.g., properties of single nodes or edges) to network-wide (e.g., path-length). This thesis presents applications of various network patterns to understand mechanisms in two complex biological systems: genome-wide co-mutation network and protein-protein interaction (PPI) network. In the first, our genome-wide analysis of co-mutation networks (Chapter 2 and 3) unites two strands of recent, significant evolutionary biological inquiries: to obtain a quantitative estimate of the prevalence of epistasis in long-term human mitochondrial (mt) genome evolution and to deduce functional organization of complex biological networks formed by these linkedgenotypes. Herein, we argued that subpopulation-based factors such as intra-species evolution do exert selection on mitochondrial genomic positions by favouring specific epistatic genetic variants. On the technical front, we characterized the evolution of co-mutation frequency against network parameters and provided a method to identify epistatic interactions in a genome dataset. Functional organization of epistasis was characterized using hierarchical modularity, which is a crucial agent for a nucleotide co-mutation network make-up. In the second, we explored three different PPI systems (Chapter 3,4,5) constructed using empirical data with an ultimate aim towards untangling the complexity in the underlying complex systems. We showed that we could use network measures such as degree-degree correlation and node duplication to describe similarity and changes between healthy and cancer Oral tissues. We also explored symmetrical patterns corresponding to 􀀀1 eigenvalues and proposed multicancer biomarkers using weighted PPI networks. The biological complexity arising during the development of C. elegans, from single-cell embryo to the multicellular completely developed organism, were studied using proteome data and network science tools. Network measures such as degree-degree correlation, edge-betweenness centrality and Von Neumann entropy were used to demonstrate differences in organizational principles during the organisms life cycle. Objectives (a) To realize empirical data drawn from genomic and proteomic interactions in the form of networks and extend the tools of network science and spectral graph theory towards identifying characteristic signatures of biological systems. (b) To obtain a quantitative estimate of the prevalence of compensatory mutational interactions in genome evolution and to deduce functional organization (e.g. motifs and modules) of complex biological networks formed by linked-genotypes. (c) To uncover the role and importance of interaction patterns in the occurrence of network-based structures (e.g. node duplication, network symmetry).