@article {47993,
	title = {Emerging landscape of molecular interaction networks: opportunities, challenges and prospects},
	journal = {Journal of Biosciences},
	volume = {47},
	year = {2022},
	month = {APR},
	pages = {24},
	type = {Review},
	abstract = {<p>
	Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug-disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.</p>
},
	keywords = {Centrality, disease mechanisms, hybrid network-based models, machine learning, molecular interaction networks, network topology, systems biology},
	issn = {0250-5991},
	doi = {10.1007/s12038-022-00253-y},
	author = {Panditrao, Gauri and Bhowmick, Rupa and Meena, Chandrakala and Sarkar, Ram Rup}
}
@article {48836,
	title = {Emergent stability in complex network dynamics},
	journal = {Nature Physics},
	volume = {19},
	year = {2023},
	month = {JUL},
	pages = {1033+},
	type = {Article},
	abstract = {<p>
	The stable functionality of networked systems is a hallmark of their natural ability to coordinate between their multiple interacting components. Yet, real-world networks often appear random and highly irregular, raising the question of what are the naturally emerging organizing principles of complex system stability. The answer is encoded within the system{\textquoteright}s stability matrix-the Jacobian-but is hard to retrieve, due to the scale and diversity of the relevant systems, their broad parameter space and their nonlinear interaction dynamics. Here we introduce the dynamic Jacobian ensemble, which allows us to systematically investigate the fixed-point dynamics of a range of relevant network-based models. Within this ensemble, we find that complex systems exhibit discrete stability classes. These range from asymptotically unstable (where stability is unattainable) to sensitive (where stability abides within a bounded range of system parameters). Alongside these two classes, we uncover a third asymptotically stable class in which a sufficiently large and heterogeneous network acquires a guaranteed stability, independent of its microscopic parameters and robust against external perturbation. Hence, in this ensemble, two of the most ubiquitous characteristics of real-world networks-scale and heterogeneity-emerge as natural organizing principles to ensure fixed-point stability in the face of changing environmental conditions. Despite looking highly irregular, most real-world networks exhibit natural stability to external perturbations. A study of the properties of the stability matrix of networks now sheds light on the principles underlying this emerging stability.</p>
},
	issn = {1745-2473},
	doi = {10.1038/s41567-023-02020-8},
	author = {Meena, Chandrakala and Hens, Chittaranjan and Acharyya, Suman and Haber, Simcha and Boccaletti, Stefano and Barzel, Baruch}
}
