Network Analysis of Mitochondrial Genome

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The identity of an individual entity lies in the wholeness of the system in which it is present. We observe numerous complex phenomena happening around us, and to study them, we de¿ne them as systems with particular entities leading to the commencement of those phenomena. Modelling these complex systems gives rise to the formation of complex networks. These networks represent the meaningful connections between the entities of the complex system. "I think the next century (21st) will be the century of complexity", once said Stephen Hawking in light of the omnipresence of complex systems around us. The past two decades observed the immense potential of network science due to its holistic approach, ¿exibility, and applications to vast ¿elds of scienti¿c research. Network science has provided various models and algorithms under the umbrella of statistical physics to analyze natural and social sciences, including complex biological systems. Like any other physical system, it is also required to identify and characterize the individual building blocks in complex biological systems and obtain and establish insights into the interactions. The biological complex systems can be de¿ned by multiple types of entities such as biomolecules (proteins and genes), pathways (metabolic, anabolic, and disease), cells (neurons), tissues (brain regions), and organs (human complexome) along with their de¿ned interactions. In biological systems, interactions among cellular entities are not always straightforward as in social and physical networks. Hence, their interpretation becomes much more complicated, aided by the immense size, temporal dynamics, and non-linearity behaviour. However, the vast diversity of biological systems allows us to de¿ne them at various levels into network models.